Including EMS Young Scientist Awardee 2026
This session aims to bring together the latest research quantifying and understanding high-impact climate extremes in past, present and future climates. We welcome studies across all spatial and temporal scales, and covering compound, cascading, and connected extremes as well as worst-case scenarios, with the ultimate goal to provide actionable climate information about their drivers, future changes and implications to increase societal preparedness to such extreme high-impact events.
We invite work addressing high-impact extremes via, but not limited to, model experiments and intercomparisons, diverse storyline approaches such as event-based or dynamical storylines, climate projections including large ensembles and unseen events, insights from paleo archives, and attribution studies. We also especially welcome contributions focusing on physical understanding of high-impact events, on their ecological and socioeconomic impacts, as well as on approaches to potentially limit societal impacts.
Recently developed data-driven weather models demonstrate accuracy comparable to physical models while being computationally cheaper. Nonetheless, their efficacy in modeling extreme weather conditions in present day and their applicability in future warmer scenarios remains largely unknown. This study evaluates the performance of various global data-driven weather models in predicting the peak of the 2019 European summer heatwave, compared to the physics-based ICON model. The evaluation not only encompasses the present climate but also aims to assess the performance of these models in emulating this specific event in a +2, +3, and +4K warmer world relative to pre-industrial levels, following a storyline framework. Both data-driven and ICON models are initialized with global storyline simulations from the AWI-CM1 model for current and future warmer scenarios. To quantify the sensitivity to initial condition uncertainties, the random field perturbation technique is employed in the data-driven models. The findings indicate that the majority of data-driven models inadequately predict maximum temperatures in the heatwave core region under present climatic conditions as compared to ICON, exhibiting an underestimation of up to 2°C, even within an ensemble framework. These models are also assessed against ICON under future warmer climatic scenarios to determine their generalization capabilities outside the training distribution. The results indicate that these models can capture temperature amplification across climate scenarios to some extent, they underestimate the accelerated warming rate during the heat wave compared to ICON. This highlights both the potential and limitations of data-driven models for their applicability in future warmer scenarios and emphasizing the necessity to develop hybrid strategies combining physics with data-driven techniques to enhance predictive accuracy in a changing climate.
How to cite: Namdev, P., Benitez, A. S., Debus, C., Götz, M., Klimiuk, T., Lerch, S., Ludwig, P., and Quinting, J.: A Storyline-based Evaluation of Data-Driven Weather Models for Simulating the 2019 European Summer Heatwave under Future Warming Scenarios, EMS Annual Meeting 2026, Utrecht, Netherlands, 6–11 Sep 2026, EMS2026-486, https://doi.org/10.5194/ems2026-486, 2026.
Rising global temperatures have strongly affected South America, increasing drought frequency, duration, and temperatures. Accompanied by ENSO influences, the 2015-2024 decade featured a series of high-impact heat-related extremes that led to episodes of water scarcity, widespread wildfires, power outages, crop failures, among other socioeconomic impacts in several countries. The impacts of individual extreme events can vary substantially between countries, depending on differences in infrastructure and adaptation measures. Therefore, a systematic, country-level attribution of events of the last decade can provide further understanding of how global warming could affect the occurrence of heat extremes in alternative climates at a national scale.
Using spectrally nudged storylines, we attribute the role of anthropogenic climate change by simulating historical events in cooler (pre-industrial) and warmer (+2K, +3K, +4K warming levels) climates. Here, we extend the approach beyond the usual single event attribution and analyze extremes of the entire decade. We focus our analysis on climate indices relevant to human health (warm days, warm nights, hot-humid days) and water security (extreme drought). Our results reveal a transition towards near-permanent conditions for some indices already in a +2K climate, such as year-long warm nights in Colombia, and months of extreme drought in Brazil and Bolivia. In future climates, all tropical and subtropical countries reach permanent conditions in at least one index. Southernmost countries like Chile and Argentina show summer-long extremes, while Uruguay stands out as a country with low sensitivity to global warming. Furthermore, different to present-day climate, under warmer conditions, years with or without the influence of El Niño show a comparable behavior. These findings underscore the need for country-specific adaptation strategies in a warming climate.
How to cite: León-FonFay, D., Schachtschneider, R., van Garderen, L., and Feser, F.: Storylines of the 2015-2024 heat-related extremes in South America: a country-level attribution , EMS Annual Meeting 2026, Utrecht, Netherlands, 6–11 Sep 2026, EMS2026-624, https://doi.org/10.5194/ems2026-624, 2026.
Europe experienced exceptionally hot and dry summers during 2018-2022, with severe impacts from compound heat and drought. Storyline simulations have shown that, under similar large-scale circulation, summer heat extremes can be strongly amplified with increased global warming. However, the processes controlling the magnitude and spatial variability of this amplification are still not fully understood. Here, we analyse circulation-nudged regional climate storylines for the summers 2018-2022 over Europe produced with a global-to-regional model chain (AWI-CM-1.1-MR downscaled with ICON-CLM), spanning five climate states from pre-industrial conditions to +4 K global warming. Using the advantage of the nudged storylines to provide the local climate change signal on a daily timescale, we quantify the thermodynamic response at each grid point by computing the increase in daily maximum temperature per degree of global warming (warming amplification). We found that, for the most extreme events of the heatwave-drought series, the highest warming amplification (up to 3 K/K) tends to occur outside the heatwave core, whereas the warming amplification at the hottest locations is close to 2 K/K. To explain this behaviour, we investigate the relationship between soil moisture and evaporative fraction as a diagnostic of land-atmosphere coupling regimes. The strongest amplification occurs where warming shifts land surface conditions from an energy-limited to a moisture-limited evapotranspiration regime. In these transition regions, enhanced soil moisture depletion suppresses evaporative cooling and increases sensible heating, leading to amplified temperature increases. By contrast, amplification is smaller where conditions are already moisture-limited or remain energy-limited. Our results identify evapotranspiration regime transitions as a key mechanism controlling the amplification of future European heat extremes and highlight the importance of land-atmosphere coupling for understanding regional differences in heatwave intensification.
How to cite: Klimiuk, T., Sanchez-Benitez, A., Ludwig, P., and G. Pinto, J.: Regional storylines reveal evapotranspiration regime transitions as a key driver of amplified European heat extremes, EMS Annual Meeting 2026, Utrecht, Netherlands, 6–11 Sep 2026, EMS2026-547, https://doi.org/10.5194/ems2026-547, 2026.
Heat waves are becoming not only more intense, frequent, and longer-lasting due to climate change, but they are also increasingly occurring outside their typical seasonal window. A recent example is the March 2026 heat wave in the United States, during which temperatures exceeded 30 °C at numerous stations and broke all-time March records. Similar events have been observed across mid-latitudes; for instance, Europe experienced unusually high, summer-like temperatures in late March and April 2024. Despite these emerging trends, a consistent methodological framework for analysing such events is still lacking.
Heat waves are traditionally defined as a summer (June–August) phenomenon, while warm spells are typically identified using relative temperature thresholds. However, this approach does not adequately capture events in which absolute temperatures reach hazardous levels outside the summer season. As a result, potentially high-impact early- or late-season extremes have not yet received sufficient scientific attention.
In this study, we introduce and evaluate the concept of off-season heat waves. This concept is based on two key principles: (i) such events occur outside the summer period, with timing that is region-specific and reflects local climate conditions; and (ii) the absolute temperatures reached during these events are comparable to those observed during typical summer heat waves. The applicability of this concept is assessed across Europe using E-OBS gridded temperature data for the period 1950–2025. Given the diversity of climatic zones across the continent, we first determine the timing and duration of climatological seasons for each region, allowing for a consistent distinction between off-season and summertime heat waves. We then identify corresponding absolute temperature thresholds and examine their spatial variability. Finally, we present the spatial and temporal patterns of the most prominent off-season heat waves.
How to cite: Plavcová, E. and Lhotka, O.: The concept of off-season heat waves, EMS Annual Meeting 2026, Utrecht, Netherlands, 6–11 Sep 2026, EMS2026-215, https://doi.org/10.5194/ems2026-215, 2026.
The role of surface-atmosphere interaction during extreme heat events has been increasingly studied in recent years, focusing on its impact in terms of surface and near-ground temperature. This has revealed coupling dynamics that can enhance extreme temperatures when the soil is dry¹. Soil moisture has also been shown to affect thermal circulation dynamics in urban areas².
References
1. Miralles, D. G., Teuling, A. J., Van Heerwaarden, C. C. & Vilà-Guerau De Arellano, J. Mega-heatwave temperatures due to combined soil desiccation and atmospheric heat accumulation. Nature Geosci 7, 345–349 (2014).
2. Tabassum, A., Hong, S.-H., Park, K. & Baik, J.-J. Impacts of Changes in Soil Moisture on Urban Heat Islands and Urban Breeze Circulations: Idealized Ensemble Simulations. Asia-Pac J Atmos Sci 60, 541–553 (2024).
How to cite: Bentivoglio, G., Ruggieri, P., and Di Sabatino, S.: The multivariate role of surface-atmosphere interaction in urban heat stress, EMS Annual Meeting 2026, Utrecht, Netherlands, 6–11 Sep 2026, EMS2026-524, https://doi.org/10.5194/ems2026-524, 2026.
Expectations for new temperature records derived from climate projections have recently been outpaced by record-shattering events. Such events pose a serious hazard to societies Not all physical mechanisms are well-understood. Open questions also remain on the coordinated and uncoordinated human responses. Insights into societal reactions to such outlier records are important for designing adaptation strategies, and for anticipating societal dynamics.
We hypothesize that extreme heat events may trigger societal response. Therefore, we design a statistical framework to explore heat record exceedance in recent decades and combine it with socioeconomic impact and response data to elucidate event-response relationships. More specifically, we assess air conditioning uptake in Europe and heat-health impacts. As meteorological baseline we use daily maximum temperature and compare it with annual air-conditioning data at country-level, global burden of disease reports, and socio-economic variables. We validate our hypothesis using both fixed effects regression models and event coincidence analysis. We first find that while temperature records show a strong upward trend in entire Europe, the occurrence of large temperature record exceedance is spatially heterogeneous. Fixed effects analyses show a statistically significant effect of highest temperature and gross-domestic product on air-conditioning uptake. They also highlight the importance of a one-year time lag between highest temperature and the air-conditioning data. Further, event coincidence analysis points at an impact of single heat events on air-conditioning uptake.
Overall, our results attempt to shed light onto an issue that is of urgent societal importance in the face of new records. Insights into the driving role of single record-breaking events are very valuable for informing adaptation measures, wider policies, but also early warning systems and approaches related to anticipatory action.
How to cite: Lohrey, S., Falchetta, G., de Ruiter, M., Ward, P., and Kornhuber, K.: Assessing Societal Response to Extreme Temperature Shocks, EMS Annual Meeting 2026, Utrecht, Netherlands, 6–11 Sep 2026, EMS2026-776, https://doi.org/10.5194/ems2026-776, 2026.
Extreme weather events have a disproportionate impact on society and are among the most tangible manifestations of anthropogenic climate change. Their inclusion in National climate services is therefore essential for informing climate risk assessments and adaptation planning. Framing future projections through storyline-based approaches anchored in well-remembered historical events offers a powerful means of connecting climate statistics to societal experience, thereby potentially improving understanding and usability.
Here, we revisit the exceptional summer of 1976, now 50 years ago, which affected large parts of north-western Europe, including the Netherlands, Belgium, and the United Kingdom. 1976 remains one of the most severe drought and heat events in the instrumental record. The event was preconditioned by dry conditions in 1975 and the preceding winter, which depleted soil moisture and groundwater reserves, followed by persistent heatwave conditions during summer 1976 that further intensified drought through enhanced evapotranspiration.
Using Pseudo Global Warming (PGW) experiments with a regional climate model, we place the 1976 event in present-day and future climate contexts. By conditioning on the observed large-scale circulation patterns, we quantify how the intensity and duration of drought and heat would change in progressively warmer climates. This approach allows a direct comparison between historically experienced extremes and plausible future analogues, and facilitates linkage with probabilistic regional climate projections.
We aim to assess whether event-based frameworks for National Climate Scenarios and climate services can improve the clarity and effectiveness of communicating future climate risks, better inform the stress-testing of adaptation strategies, and further enhance stakeholder engagement across a range of decision-making contexts.
How to cite: van der Wiel, K., Dullaart, J., Lenderink, G., de Vries, H., van Meijgaard, E., and van Dalum, C.: Event-based learning? Revisiting the 1976 drought and heatwave in a changing climate, EMS Annual Meeting 2026, Utrecht, Netherlands, 6–11 Sep 2026, EMS2026-198, https://doi.org/10.5194/ems2026-198, 2026.
Prolonged summer droughts represent a significant and growing threat across Europe, as their persistence hinders hydrological recovery and severely impacts water resources, ecosystems, and agricultural systems under ongoing climatic warming. These extended dry periods can create soil-moisture deficits, ecological stress, and amplified heat extremes. Understanding the response of multi-year droughts to different warming levels is vital for shaping both adaptation and mitigation strategies.
In this study, we investigate the behaviour and severity of the 2018-2022 European multi-year soil moisture drought across a range of climate warming levels. We apply an innovative storyline attribution approach, which enables a physically consistent comparison of the same drought sequence under different climate conditions. Specifically, we utilise spectrally nudged AWI-CM-1-1-MR, constrained to follow observed synoptic-scale circulation from ERA5, to force the mesoscale Hydrologic Model (mHM). This modelling setup allows us to specifically isolate how anthropogenic warming modifies soil-moisture deficits, without altering the real-world atmospheric conditions that triggered the drought sequence.
Under the present-day climate conditions, the 2018-2022 drought produced a soil-moisture deficit of -44 (±11.8) km3, affecting 0.63 (±0.07) million km2 (11.5% of the study area). In the absence of anthropogenic climate change (pre-industrial climate conditions), the 2018-2022 multi-year event would have shown a soil moisture surplus nearly double the magnitude of present-day losses, with drought spatial extent only about one-third of current levels. Future warming levels further exacerbate these impacts. With warming of 2 K to 4 K, the losses increase from -82 (±6.6) to -256 (±7.1) km3, while drought extent expands from approximately 16% to 43%.
Overall, our results demonstrate that rising global temperatures substantially intensify multi-year droughts by both enlarging their spatial footprint and deepening hydrological deficits. As climate warming increases the likelihood that single-year droughts transition into persistent multi-year events, the findings emphasise the urgent need for effective climate mitigation and adaptation strategies across Europe.
How to cite: Kettaren, R., Sanchez-Benitez, A., Goessling, H., Athanase, M., Kumar, R., Samaniego, L., and Rakovec, O.: Storyline-Based Climate Attribution Reveals Strong Intensification of 2018–2022 Multi-Year Droughts in Europe, EMS Annual Meeting 2026, Utrecht, Netherlands, 6–11 Sep 2026, EMS2026-231, https://doi.org/10.5194/ems2026-231, 2026.
Multi-year droughts are severe natural hazards that have become more common due to climate change. Their longevity and resulting impact distinguish them from shorter seasonal or sub-annual drought events. This persistence in drought events suggests an important role for memory in the climate system, but could also be caused by the coincidental alignment of consecutive dry years. Since multi-year droughts are relatively rare and research has mostly focused on individual case studies, general multi-year drought drivers remain poorly understood.
In previous work, we identified different regional influences from the ocean, atmosphere and land surface correlating with multi-year droughts onset, with lags up to several months in prior to drought onset. Building on this, we here investigate how land-atmosphere and ocean-atmosphere interactions shape multi-year drought frequency, duration, and periodicity on annual to multi-decadal timescales. For this, we have set up a set of global climate model experiments performed with EC-Earth3, designed to selectively enable or suppress land-atmosphere and ocean-atmosphere coupling, both on global and regional scales. This allows us to directly assess the influences of ocean-atmosphere and land-atmosphere coupling, memory in the ocean and land, and the role of climate variability on different drought characteristics from annual to multi-decadal time scales.
By comparing multi-year droughts to shorter drought events across these experiments, we can quantify the extent to which different interactions actively promote the occurrence of multi-year droughts. Our results will provide new insight into the influence of climate interactions and variability on multi-year droughts and clarify the potential limits of predictability for multi-year droughts on regional and global scales.
How to cite: van Mourik, J., van der Wiel, K., le Sager, P., Hazeleger, W., and Wanders, N.: Disentangling the influences of ocean- and land-interactions on multi-year droughts, EMS Annual Meeting 2026, Utrecht, Netherlands, 6–11 Sep 2026, EMS2026-494, https://doi.org/10.5194/ems2026-494, 2026.
Recent European wildfire seasons reinforced the urgency of understanding fire weather extremes, characterized by hot, dry, and windy conditions. While previous studies have primarily focused on individual events or anthropogenic attribution, the physically plausible range of fire weather extremes and the contribution of internal variability remain less well explored. We address this gap by using large ensemble climate simulations to comprehensively sample internal variability and to quantify the underlying conditions of extreme and most extreme fire weather - atmospheric circulation, soil moisture and sea surface temperatures - at different spatial and temporal scales. We analyze fire weather extremes during the fire season (May-October) in Southwest Europe using the CMIP6 MPI Grand Ensemble (MPI-GE, 50 members) and ERA5 reanalysis. We evaluate temperature and humidity conditions during extreme events in MPI-GE against ERA5 to assess the representation of climate conditions associated with observed extremes in the model, thereby providing physical grounding for unprecedented events simulated in the ensemble.
In MPI-GE, the most extreme fire weather events are more persistent than in ERA5, with durations of up to two months, and can exhibit spatial extents up to 20% larger than the largest ERA5 events. Despite extending beyond the observational record, the distributions of event duration and spatial extent remain comparable between MPI-GE and ERA5, supporting the use of large ensembles to investigate rare high-impact events. In both datasets, extreme events occur under compound atmospheric conditions characterized by enhanced vapour pressure deficit, depleted soil moisture (SM), and positive geopotential height at 500 hPa (Z500) anomalies. Differences in Z500 anomaly strength and spatial pattern among the most extreme events in MPI-GE are small, indicating that increasing severity does not arise from fundamentally different atmospheric regimes. Positive adjacent North Atlantic sea surface temperature anomalies are detectable already in spring, particularly for the most severe events, while multi-year SM conditions vary substantially, showing that both seasonal drying and persistent multi-year drought can lead to extreme fire weather. Overall, our results indicate that the most severe fire weather events arise from amplified thermodynamic preconditioning within similar large-scale atmospheric conditions, highlighting potential predictability of extreme fire weather risk and opportunities for improved preparedness.
How to cite: Dietz, V., Müller, W., Borchert, L., and Suarez-Gutierrez, L.: What Drives the Most Extreme Fire Weather in Europe? A Large-Ensemble Perspective, EMS Annual Meeting 2026, Utrecht, Netherlands, 6–11 Sep 2026, EMS2026-345, https://doi.org/10.5194/ems2026-345, 2026.
The catastrophic January 2025 Los Angeles fires highlight the growing importance of compound hazards arising from hydroclimatic intensification under climate change. This study investigates how sequences of anomalously wet and dry conditions interact to create high-impact wildfire events, framing extreme fires as outcomes of cascading and compounding hydroclimatic processes rather than isolated meteorological extremes.
We identify a recurrent hydroclimatic rebound mechanism, in which prolonged moist anomalies (lasting 6–27 months) promote vegetation growth and fuel accumulation, followed by a rapid drying in the months preceding ignition. This drying phase, characterised by negative Standardized Precipitation Evapotranspiration Index anomalies, increasing vapour pressure deficit, and declining soil and fuel moisture, transforms accumulated biomass into highly flammable fuel. The combination of these sequential processes constitutes a compound hazard, where antecedent wetness and subsequent drought jointly amplify wildfire risk.
This mechanism is particularly pronounced in fuel-limited Mediterranean and desert Californian biomes, including events such as the Palisades and Eaton fires, where hydroclimatic rebound emerges as a key driver of extreme fire behaviour. In contrast, forested mountain regions exhibit a different compound structure, with fire activity primarily governed by prolonged drought and short-term fire weather conditions, and weaker influence from antecedent moistening.
Our findings demonstrate that hydroclimatic intensification operates through long-memory processes that propagate across the atmosphere–soil–vegetation continuum over timescales of up to two years. These results extend existing drought propagation frameworks by explicitly linking them to fire danger and highlighting biome-dependent pathways of risk amplification.
Incorporating these compound and lagged hydroclimatic signals into fire prediction systems significantly improves the representation of fuel conditions and enhances the predictability of extreme wildfire events. This underscores the need to move beyond traditional fire weather indices and adopt a compound hazard perspective that integrates both short-term meteorological drivers and long-term hydroclimatic variability. Such an approach is essential for anticipating and managing increasingly extreme wildfire regimes in a warming climate.
How to cite: McNorton, J., Di Giuseppe, F., and Keune, J.: Hydroclimatic intensification as a compound driver of extreme wildfires, EMS Annual Meeting 2026, Utrecht, Netherlands, 6–11 Sep 2026, EMS2026-309, https://doi.org/10.5194/ems2026-309, 2026.
The worlds food system is highly dependent on just a few crops, with 60% of caloric intake coming from maize, soya beans, wheat, and rice. Most of these crops are produced in just a few regions, the so-called breadbaskets of the world, making the global food system vulnerable to multi-crop multi-breadbasket failures. Moreover, climate change is increasing the chance of crop failures, due to an increase in compounding hot and dry extremes. Starting from the impact, we use an innovative data-driven method called Archetype Analysis to identify crop yield losses. We show that global multiple-crop multi-breadbasket (MCMB) yield failure is associated with specific climatic fingerprints, including hot-dry conditions, wave-like anomalies in the atmospheric circulation, and distinct sea surface temperature (SST) imprints across the globe. We identify so-called adverse archetypes, recurring representing global crop yield losses, for each individual crop type and all of them combined. These adverse archetypes are accompanied by simultaneous hot-dry-surface imprints across the world, highlighting these high-risk crop failure scenarios are driven by climate extremes. Worryingly, we find that the accompanying SST fingerprints of MCMB failure are increasing in frequency in observations, consisting of La Niña like and negative PDO conditions. However, the response of these climate modes of variability due to anthropogenic activities is not yet fully understood and climate models often inaccurately reproduce the observed SST and atmospheric circulation trends. Thus, the fact that our results indicate that simultaneous crop failures are linked to distinct SSTs and atmospheric circulation anomalies, highlights the deep uncertainty we currently face regarding food security in the future.
How to cite: Happé, T., Hamed, R., Anderson, W., Chapman, C., Scussolini, P., and Coumou, D.: Climate fingerprints of Multi-Crop Multi-Breadbasket failures , EMS Annual Meeting 2026, Utrecht, Netherlands, 6–11 Sep 2026, EMS2026-627, https://doi.org/10.5194/ems2026-627, 2026.
Severe convective storms are among the most damaging weather hazards in Spain, often producing extreme precipitation, flash floods, large-to-giant hail, tornadoes and severe convective wind gusts. These hazards are especially relevant in the Mediterranean region, where complex terrain, very warm
sea surface temperatures, strong low-level moisture transport and cut-off lows can interact to promote high-impact events. During the last decades, Spain has experienced several extreme convective episodes with remarkable socio-economic impacts. In addition, our database of large hail events shows an increase in the frequency of large hailstorms, raising the question of whether anthropogenic climate change is already modifying the physical environments in which these hazards develop.
Here, we analyze the role of climate change in recent extreme convective events in Spain using a pseudo global warming storyline approach. High-resolution numerical simulations of observed events are repeated under perturbed thermodynamic conditions representative of different climatic states. This framework allows us to assess how changes in moisture availability, instability, microphysical processes, vertical motions and storm dynamics affect the intensity and destructive potential of highimpact convective storms.
This approach is applied to three recent events with very different surface impacts. The first case study focuses on the giant hailstorm that affected northeastern Spain on 30 August 2022, producing hailstones up to 12 cm in diameter, the largest documented in Spain. Results indicate that the present climate provided a more favourable thermodynamic environment for severe hail than a preindustrial-like climate (Martín et al. 2024), while future warming would further increase the likelihood of very large hailstorms. The second case study examines the catastrophic Valencia flash floods of 29 October 2024, associated with a cut-off low and rainfall accumulations exceeding 300 mm over a broad area and locally reaching 771 mm in 24 h. Storyline simulations show that current anthropogenic climate conditions significantly intensified sub-daily rainfall through amplified storm dynamics, such as stronger vertical motions or enhanced microphysics activity (Calvo-Sancho et al. 2026). The third case study addresses the severe multi-hazard supercell outbreak of 6 July 2023 in the Ebro Valley, where storms produced giant hail, a tornado, destructive downburst winds and flash flooding (Calvo-Sancho et al. 2026).
Overall, these results highlight the growing threat posed by severe convective storms in a warming Mediterranean climate, emphasizing how high-resolution numerical modeling is essential to strengthen adaptation strategies, improve early-warning systems, and reduce societal vulnerability to extreme rainfall, severe hail and compound convective hazards.
References:
Martín, M. L., Calvo-Sancho, C., Taszarek, M., González-Alemán, J. J., Montoro-Mendoza, A., Díaz-Fernández, J., et al. (2024). Major role of marine heatwave and anthropogenic climate change on a Giant hail Event in Spain. Geophysical Research Letters, 51, e2023GL107632. https://doi.org/10.1029/2023GL107632
Calvo-Sancho, C., Díaz-Fernández, J., González-Alemán, J.J. et al. Human-induced climate change amplification on storm dynamics in Valencia’s 2024 catastrophic flash flood. Nat Commun 17, 1492 (2026). https://doi.org/10.1038/s41467-026-68929-9
Calvo-Sancho, C., González-Alemán, J. J., Halifa-Marín, A., Martín, M. L., and Azorin-Molina, C.: Future intensification of severe multi-hazard supercells in a semi-arid environment of Southern Europe, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-18069, https://doi.org/10.5194/egusphere-egu26-18069, 2026.
How to cite: Calvo-Sancho, C.: From giant hail to flash floods: is climate change intensifying extreme convective events in Spain?, EMS Annual Meeting 2026, Utrecht, Netherlands, 6–11 Sep 2026, EMS2026-829, https://doi.org/10.5194/ems2026-829, 2026.
In recent decades, the Euro–Mediterranean region has experienced a marked increase in catastrophic summer climate extremes, including persistent record-breaking atmospheric and marine heatwaves, and destructive convective events such as long-lived mesoscale convective systems (derecho) and supercells with unparalleled hail-size. All these have provoked severe socioeconomic, ecological and human impacts. While these phenomena are often studied separately, their frequent co-occurrence suggests the influence of common large-scale circulation drivers, which remain actively debated.
Building on recent work linking North Atlantic freshwater anomalies to downstream atmospheric circulation responses, this ongoing study explores whether part of the recent European summer climate signal may be influenced by remote hemispheric-scale forcing associated with Greenland Ice Sheet mass loss, which has also coincidentally accelerated in recent decades due to anthropogenic influences. This linkage was not initially targeted but emerged unexpectedly from exploratory diagnostics motivated by broader investigations of North Atlantic variability. Preliminary results indicate that periods of enhanced summer Greenland melt tend to coincide with subsequent anomalous spring–summer circulation patterns over the Euro-Atlantic sector that favour persistent ridging and blocking-like conditions over the Euro-Mediterranean region. Such circulation states are consistent with environments conducive to prolonged heat stress, the development of marine heatwaves, and subsequent severe convective outbreaks.
Initial comparisons with global climate models from CMIP6 suggest that this potential pathway is poorly represented, possibly due to limitations in simulating localized freshwater forcing and its coupled atmosphere–ocean effects, which indicates that current projections of future climate may be underestimating these impacts. Our findings would point out Greenland melting as a previously unreported major driver of spring-summer large-scale circulation changes. Incorporating these processes could then be essential for forecasts systems and long-term projections, likely posing a significant gap in our ability to project future risk. Ongoing work focuses on testing the robustness of this emerging signal, clarifying its relevance relative to other known drivers of European summer extremes and exploring its hemispheric-scale reach.
How to cite: González-Alemán, J. J., Oltmanns, M., González-Herrero, S., Vitard, F., Donat, M., Doblas-Reyes, F., Barriopedro, D., Riboldi, J., Calvo-Sancho, C., Jiménez-Esteve, B., Cos, P., and Wehner, M.: Emerging evidence of Greenland Ice Sheet melt influence on recent Euro-Mediterranean record-breaking heat and convective storms, EMS Annual Meeting 2026, Utrecht, Netherlands, 6–11 Sep 2026, EMS2026-601, https://doi.org/10.5194/ems2026-601, 2026.
Impacts resulting from extreme weather and climate events depend on a multitude of factors, such as event intensity, and the level of risk awareness and preparation of the affected region. Climate change exacerbates extreme temperatures and extreme rainfall in most land regions, yet, natural variability can regionally mask or amplify the forced trend for years or decades. In regions where the forced trend of a certain extreme has been masked for an extended period, local communities may be inexperienced with, unaware of, and unprepared for record extremes that exceed the most extreme event in history. Global mean temperatures are currently warming at a rate unprecedented in the observational record. The ratio of occurrence of daily surface temperature records since 1950 relative to the theoretically expected occurrence in a stationary climate, is now about 3–3.5 for hot records globally [Fischer et al. (2025)]. The forced signal in annual maximum daily precipitation rates lags behind the forced signal in temperature, with a global record occurrence ratio of about 1.5 [Fischer et al. (2025)]. To prepare for future record heat and rainfall, it is crucial to identify regions where the probability of breaking or shattering the local standing record is highest. Here, we aim to quantify which regions are exposed to particularly high risk of “surprise extremes” in temperature and rainfall, taking into account local extreme event history.
We quantify local record-breaking probabilities conditional on the standing record level and identify hotspots of high near-term record-breaking probability, using several statistical detection algorithms and extreme value theory. The forecast skill and robustness of our method is evaluated using several climate models, and applied to observations and reanalysis for real world record projections.
The global pattern of near-term conditional record-breaking probabilities shows clear signatures of the forced climate change pattern, with strong modifications attributable to natural variability. It is these modifications that are most relevant for near-term risks. The conditional probability of settinga new record is particularly high in years and regions where the forced trend has been masked by unforced natural variability for (multi-)decadal periods, leading to little to no detectable forced trend and absence of record breaking. Ironically, it is thus often regions where recent reminders of climate change were absent that deserve particular attention with regard to preparing for high impact record-breaking extremes. Quantitative estimates of conditional record-breaking probability are uncertain; the largest source of uncertainty lies in the separation of historical trends into forced response and internal variability. We evaluate different methods to estimate the forced response and to determine non-stationary extreme value distributions. While the exact conditional probability is uncertain, we find that hotspot regions where the conditional record probability is high can be robustly identified.
How to cite: de Vries, I., Fischer, E., Sippel, S., and Huser, R.: Most at-risk regions for near-term surprise extremes, EMS Annual Meeting 2026, Utrecht, Netherlands, 6–11 Sep 2026, EMS2026-798, https://doi.org/10.5194/ems2026-798, 2026.
Off-season tropical cyclones in the western North Pacific, occurring between December and April, are rare, but their impacts can be catastrophic. Unlike peak-season storms, they strike when disaster preparedness is reduced, public awareness is limited, and forecasting efforts are deprioritized. Recent decades have seen a troubling rise in these events, with Typhoon Bopha (2012), Typhoon Nock-ten (2016), and record-breaking Typhoon Wutip (2019) serving as stark reminders of their destructive potential. Understanding what is driving this increase is therefore an urgent scientific and societal priority.
This study examines the multi-scale physical mechanisms behind the observed increase in off-season landfalling tropical cyclone frequency in the western North Pacific over 1981 to 2022. We find that genesis locations have shifted significantly westward at a rate of 0.60 degrees per year, bringing more storms into coastal-proximate regions where landfall probability is higher, while the overall number of off-season cyclones has remained stable.
Three interacting mechanisms drive this spatial reorganization. Convectively coupled equatorial Rossby waves act as short-timescale triggers, creating windows of opportunity for cyclogenesis through reduced vertical wind shear, enhanced low-level moisture and increased convective ascent. Over 70% of landfalling off-season tropical cyclones form during dynamically supportive wave phases, with a fourfold increase in daily formation probability compared to inactive phases. On decadal timescales, positive phases of the Interdecadal Pacific Oscillation correlate strongly with landfalling cyclone frequency through steering flow modifications that direct storms toward Asian coastlines. At the longest timescale, persistent asymmetric Pacific warming has expanded the Western Pacific Warm Pool northwestward, systematically shifting the most favorable cyclogenesis regions toward densely populated coastlines.
These findings highlight a compounding risk scenario: more frequent landfalls, reduced lead times for preparation, and populations caught off-guard during a period of historically low vigilance. As climate change continues to reshape tropical ocean temperatures, off-season coastal communities across Southeast and East Asia face growing exposure to high-impact events that fall outside traditional risk frameworks.
This work is published open access: https://doi.org/10.1038/s41612-026-01349-0
How to cite: Latos, B., Moon, I.-J., and Heidemann, H.: Multi-scale drivers of increasing off-season tropical cyclone landfalls in the western North Pacific, EMS Annual Meeting 2026, Utrecht, Netherlands, 6–11 Sep 2026, EMS2026-695, https://doi.org/10.5194/ems2026-695, 2026.
Extratropical cyclones are among the most damaging weather systems in midlatitudes, yet quantifying the influence of anthropogenic climate change (ACC) on individual storms remains challenging due to the interplay between thermodynamic and dynamical processes. Here, we assess the potential of AI-based weather prediction (AIWP) models to both forecast and attribute ACC signals in two high-impact European extratropical cyclones with contrasting characteristics: storm Ciarán (November 2023), which underwent explosive cyclogenesis and extreme winds, and storm Claudia (November 2025), characterized by an intense atmospheric river.
We evaluate four state-of-the-art AIWP models and benchmark them against the operational forecasts of ECMWF Integrated Forecast System (IFS), finding that all AIWP systems skillfully reproduce the large-scale evolution of both storms several days in advance, albeit with event-dependent performance. Building on this skill, we apply a forecast-based storyline attribution framework in which factual forecasts initialized from ERA5 are compared with counterfactual simulations generated by applying a pseudo–global warming (PGW) perturbation derived from CMIP6 historical simulations to the model initial conditions.
The attribution analysis reveals physically coherent ACC fingerprints across sea-level pressure, low-level winds, moisture, and precipitation. Moisture-related signals are robust across models for both storms, while wind and circulation responses show greater model and event dependence, reflecting the differing levels of robustness in thermodynamic versus dynamic responses to climate change. Precipitation attribution using the ECMWF Artificial Intelligence Forecasting System (AIFS) indicates ACC-driven increases in accumulated rainfall for both events, consistent with near–Clausius–Clapeyron thermodynamic scaling at the regional scale, but with locally larger increases arising from event-dependent dynamical adjustments. These results demonstrate that AIWP models enable fast, event-specific climate change attribution within the forecast window, supporting near–real-time assessments of ACC influences on high-impact extratropical cyclones and opening new opportunities for operational climate services.
How to cite: Jiménez-Esteve, B., Barriopedro, D., and García-Herrera, R.: Forecast-based Attribution of Climate Change Signals in High-Impact Extratropical Cyclones Using AI Weather Models, EMS Annual Meeting 2026, Utrecht, Netherlands, 6–11 Sep 2026, EMS2026-209, https://doi.org/10.5194/ems2026-209, 2026.
The Northeast United States has experienced the most rapidly increasing occurrences of extreme precipitation within the U.S. over recent decades, particularly during the warm season. This historical trend is primarily linked to events associated with tropical cyclones. Understanding the drivers leading to long-term trends in regional extreme precipitation under different future climate scenarios is critical to adaptation and mitigation planning.
New simulations from the fully-coupled 25-km GFDL SPEAR model and its 10 ensemble members, present a unique opportunity to study changes in regional extreme precipitation and relevant physical processes. Under the SSP5-8.5 scenario, SPEAR projects that the frequency of events exceeding the historical top-1% precipitation threshold in the Northeast U.S. will increase by up to 2.4% by the end of the 21st century. The projected increase is driven by higher anthropogenic radiative forcing and is distinguishable from natural variability by the mid-century. From the meteorological perspective, the occurrences of warm season extreme precipitation related to both atmospheric rivers and tropical cyclones are projected to increase, even though the frequency of tropical cyclones in the North Atlantic is projected to decrease in the model.
The SSP5-8.5 scenario, however, represents a highly unlikely trajectory, prompting the scientific community to explore scenarios with rapid reductions in greenhouse gas (GHG) concentrations through various climate mitigation efforts. Using the SSP5-3.4OS overshoot scenario from the SPEAR model—where GHG emissions decline sharply after 2040 and reach net-negative levels by 2070—we assess the impact of mitigation on extreme precipitation over the Northeast U.S. Our results show that extreme precipitation frequency over the Northeast U.S. is projected to decrease as GHG concentrations decline. However, the timing of this reversal exhibits pronounced seasonality. In the warm season, extreme precipitation frequency begins to decline shortly after GHG drawdown begins. In the cold season, on the other hand, the frequency continues rising for roughly a decade after the peak global mean warming and exhibits hysteresis behavior. These results highlight the benefit of climate mitigation in reducing extreme precipitation events, but also the complexity of regional climate responses, which can be modulated by seasonality, local-scale effects, and other factors.
How to cite: Jong, B.-T., Delworth, T., Labe, Z., and Cooke, W.: Changes in extreme precipitation across the Northeast U.S. under different climate scenarios, EMS Annual Meeting 2026, Utrecht, Netherlands, 6–11 Sep 2026, EMS2026-565, https://doi.org/10.5194/ems2026-565, 2026.
Mesoscale convective systems (MCS) are organized clusters of deep convective cells that occur frequently in the tropical Amazon basin. Here, MCS constitute an important source of precipitation, contributing by ~40 – 60 % to the total. This makes MCS vital for the Amazonian water balance, which is dependent on the westward moisture transport from the Atlantic Ocean. During the transport, moisture is recycled in multiple precipitation-evaporation cycles, including the precipitation generated by the MCS. Nevertheless, extreme precipitation related to MCS systems can cause substantial economic damage through extreme rainfall, flash flooding, debris flows, severe winds and hail. Therefore, the effect of the warming climate and changing land-atmosphere interactions on MCS frequency and intensity will have important implications. However, research does not show consensus on the intensity changes in MCS in the Amazon rainforest, and whether MCSs will become more extreme due to changes in physical processes under warming climate conditions. The main shortcoming is that the current global models’ resolution is too coarse (>25 km) to explicitly resolve turbulence and moist convection. Because of this, the intensity of the changes is jeopardized by the sensitivity of these changes to the physical representation of moist deep convection. In the EU Next Generation Earth System models (NextGEMS) project, high-resolution (9 km) earth system experiments covering the Earth were performed. The novelty of these experiments is that they partly resolve the deep convection and capture the large-scale dynamics on a global scale. In this study, we analyze the Integrated Forecasting System coupled to the Finite-volumE Sea ice-Ocean Model (IFS-FESOM) of the NextGEMS project, to characterize the Amazonian MCS in the current climate (1990-2020) and project their future changes (2020-2050; SSP3-7). We do the MCS characterization in a systematic manner: To study the MCS systems, we track them based on brightness temperature and precipitation intensity with the Python FLEXible object TRacKeR (PyFLEXTKRK). Determining these MCS tracks provides insight into the current frequency, duration, and precipitation intensity of MCSs across regions and seasons. Once we have characterized the current climate, we do the same characterization for the projected future climate to determine the main climate change effects on MCS occurrence in the Amazon and their contribution to the water cycle. In the future characterizations, we emphasize extreme scenarios that produce large amounts of precipitation (95th percentile) because of their significant environmental impact.
How to cite: Huitema, A., Benedict, I., and Vila-Guerau de Arellano, J.: Kilometer-Scale Modeling of Amazonian Mesoscale Convective Systems - Characterizing Precipitation Extremes in a Changing Climate, EMS Annual Meeting 2026, Utrecht, Netherlands, 6–11 Sep 2026, EMS2026-133, https://doi.org/10.5194/ems2026-133, 2026.
This study investigates how climate-change-driven sea surface temperature (SST) anomalies influenced the extreme precipitation and moisture sources associated with Storm Boris. Between 12 and 16 September 2024, this slow-moving Vb-like cyclone produced exceptional rainfall and severe flooding across Central Europe, including more than 350 mm of accumulated precipitation within five days in parts of Austria. While previous studies have emphasized the importance of large-scale dynamics, blocking, and strong ascent for this event, the role of SST anomalies in the surrounding basins and their effect on moisture supply remain less quantified. Here, we focus on how SST changes in the Mediterranean, Black Sea, and Atlantic modified both moisture sources and precipitation intensity during Boris.
To address this, we perform a set of sensitivity experiments with the Weather Research and Forecasting (WRF) model in which SSTs in the Mediterranean, Black Sea, and Atlantic are perturbed by ±2 K, both individually and in combination. The WRF simulations are additionally configured with wind and pressure nudging over the full simulation period and without nudging during the event itself, allowing thermodynamic and dynamical effects to be better separated. To diagnose the origin and transport pathways of moisture feeding the event, we use FLEXPART-WRF in backward trajectory mode, driven by WRF output, together with a moisture source diagnostic. Air parcels arriving in the Central European target region are traced backward for up to ten days in order to identify the dominant moisture source regions contributing to the precipitation.
The analysis identifies eastern European land areas and the Mediterranean as the primary moisture source regions for Storm Boris, while the Atlantic and Black Sea provide smaller but still relevant contributions. Among the surrounding ocean basins, the Mediterranean is the dominant marine source in all experiments. The sensitivity experiments show that cooling one basin generally reduces its direct moisture contribution, but that this loss is often partly compensated by enhanced moisture uptake from another basin, indicating a redistribution of moisture sources rather than a simple overall reduction. In contrast, warming increases the overall oceanic contribution and is associated with higher precipitation. Overall, the results indicate an average precipitation increase of about 3% per kelvin of SST warming for this event, highlighting the contribution of climate-driven SST increases to the exceptional rainfall observed during Storm Boris.
How to cite: Nadeem, I., Maier, P., Formayer, H., Dütsch, M., and Messmer, M.: Sea Surface Temperature Perturbations and Moisture Source Redistribution during Storm Boris (2024) over Central Europe, EMS Annual Meeting 2026, Utrecht, Netherlands, 6–11 Sep 2026, EMS2026-240, https://doi.org/10.5194/ems2026-240, 2026.
A high intensity rainfall event in July 2021 caused catastrophic floods in several tributaries of the Meuse river resulting in widespread damage and a high number of casualties. These unprecedented summer discharges raise urgent questions on the return periods of extreme summer precipitation. Conventional frequency analysis usually relies on Extreme Value Analysis of observational records. However, these datasets typically span only 100 years, with limited coverage of rare, high magnitude summer events. Quantification of the return values of far tail summer extremes like the July 2021 event is therefore subject to large statistical uncertainty. Furthermore, standard extrapolation approaches hypothesise a single population, failing to account for the distinct drivers of rare summer extremes. This could potentially yield underestimations of both precipitation and discharge extremes, undermining flood risk assessment and resulting in poorly informed policy making.
In this study we use 10.000 years of independent ECMWF seasonal forecast (SEAS5) data to investigate the dynamics of extreme Meuse summer precipitation beyond the range of historical records. We attempt to identify common spatial and temporal patterns in the drivers of far tail extreme Meuse summer rainfall and contrast these with the drivers of typical, moderate rainfall extremes. Pronounced dynamical differences suggest the existence of a secondary statistical population. By partitioning annual extremes based on the uncovered drivers, we aim to provide more robust return values of extreme summer rainfall. This research is an attempt to move beyond standard extrapolation of observations and a first step towards a more robust framework for flood protection policies.
How to cite: van der Gaast, A., van Voorst, L., van den Brink, H., and Coumou, D.: Uncovering a hidden population in extreme summer rainfall: Process-specific extreme value analysis in the Meuse basin, EMS Annual Meeting 2026, Utrecht, Netherlands, 6–11 Sep 2026, EMS2026-503, https://doi.org/10.5194/ems2026-503, 2026.
During several periods of winter 2025/2026, some European regions experienced relatively harsh winter weather, with low minimum air temperatures. Society has perceived these conditions as extreme. However, objectively, the temperature minima were far from extremes recorded during the 20th century. With ongoing warming, even warmer cold extremes might result in considerable impacts across sectors and natural ecosystems. Here, we investigate how changes in the internal variability of winter temperature might alter the shape of its statistical distribution under stronger radiative forcing, with implications for extremes. We explore two different sets of climate model runs: LongRunMIP simulations, comparing near-equilibrium conditions under preindustrial and abrupt 4xCO2 forcings, and transient large ensemble simulations comparing the historical and scenario periods (the end of the 21st century under RCP8.5/SSP5-8.5 socio-economic pathways). A change in the shape of the temperature distribution can then point to a fundamental change in climate-governing processes. In agreement with previous studies, we reveal that a decrease in variance will accompany increasing winter mean temperatures, as day-to-day temperature variations are induced by the occurrence of synoptic-scale weather systems, and in warmer climates, this is expected to decline. Our study provides new insights, showing that the variance shrinking is spatially heterogeneous. We further focus on the skewness of the temperature distribution and investigate changes in the lengths of the cold and hot tails, which are related to changes in variance. In central, western, and northern Europe, skewness is decreasing, and the cold tail is shrinking more slowly than the hot tail, implying enduring cold extremes, even in climatic states much warmer than those we are familiar with. On the other hand, the results for the Mediterranean are almost opposite. We discuss the results in the context of recently observed temperature evolution.
How to cite: Holtanova, E., van Loon, S., Morgan, A., and Rugenstein, M.: European winters still perceived as cold, even under strong warming, EMS Annual Meeting 2026, Utrecht, Netherlands, 6–11 Sep 2026, EMS2026-508, https://doi.org/10.5194/ems2026-508, 2026.
Tropical cyclones (TCs) in the South Indian Ocean are among the most destructive hydrometeorological hazards affecting surrounding regions, including Indonesia. Reliable representation of their tracks, intensity, and structure is therefore essential for improving regional risk assessment and advancing studies of climate change. Atmospheric reanalysis datasets are widely used to investigate TC climatology and environmental conditions because they provide dynamically consistent atmospheric fields with long temporal coverage. However, previous studies have shown that reanalysis products often differ substantially in their ability to reproduce TC characteristics such as storm track, intensity, and structural evolution, with the best-performing datasets varying across ocean basins. Despite these advances, a systematic multi-dataset evaluation of tropical cyclone representation in the South Indian Ocean using a consistent and objective tracking framework remains limited. This basin is characterized by relatively sparse observational coverage, which may further influence the performance of different reanalysis products. As a result, the reliability of commonly used reanalysis datasets for representing TC characteristics in this region is still not well understood.
This study evaluates the capability of several widely used reanalysis datasets—ERA5, JRA-3Q, MERRA-2, and NCEP—to represent tropical cyclones over the South Indian Ocean. Tropical cyclone tracks are objectively detected using the CNRM tropical cyclone tracking scheme, which identifies candidate vortices based on relative vorticity, sea-level pressure minima, warm-core temperature anomalies, and wind structure before linking them into coherent trajectories. The detected tracks are paired with the International Best Track Archive for Climate Stewardship (IBTrACS) dataset to assess their consistency with observed cyclone evolution. The analysis focuses on the domain 30°E–120°E and 0°–40°S and spans multiple decades of TC activity. Dataset performance is evaluated using statistical skill metrics, including bias, root-mean-square error (RMSE), Probability of Detection (POD), False Alarm Rate (FAR), and the Critical Success Index (CSI), focusing on storm position, maximum wind speed, minimum sea-level pressure, life cycle, and peak intensity timing.
Preliminary results reveal notable differences among the datasets. JRA-3Q shows the closest agreement with IBTrACS in representing storm position and minimum sea-level pressure, outperforming other reanalysis data sets. Nevertheless, all reanalysis datasets substantially underestimate maximum wind speed, indicating persistent limitations in representing tropical cyclone intensity. These findings highlight the strengths and limitations of current reanalysis products in the South Indian Ocean and provide guidance for selecting appropriate datasets for cyclone climatology and process-based studies.
How to cite: Adrianita, F., Steeneveld, G.-J., and Permana, D.: Intercomparison and Evaluation of Reanalysis Datasets in Representing Tropical Cyclones over the South Indian Ocean, EMS Annual Meeting 2026, Utrecht, Netherlands, 6–11 Sep 2026, EMS2026-107, https://doi.org/10.5194/ems2026-107, 2026.
Wildfires are dynamic components of the Earth system, responding to both natural climate variability, ecological dynamics and human activities. While global burnt area (BA) has declined in recent decades, regional fire severity has increased, impacting ecosystems and infrastructure. Observational data, including satellite-derived products, enable monitoring of wildfire activity and the quantification of BA. However, they cannot fully separate the effects of anthropogenic climate forcing from internal climate variability. Earth system models offer a way to systematically investigate these drivers, providing critical insights into the causes of changing fire patterns.
Using a storyline approach, we separate the thermodynamic and dynamic components of climate change, largely driven by human-induced forcing, and assess their impacts on BA. This allows us to attribute wildfire responses to external forcing versus internal variability. We analyze nudged circulation simulations from the Community Earth System Model Version 2 (CESM2; Danabasoglu et al., 2020) under different anthropogenic forcing scenarios. The pre-industrial simulation is based on a CO₂ concentration of 282 ppm, whereas the historical simulation uses time-varying historical CO₂ concentrations. Both simulations are nudged to horizontal winds from the ERA5 reanalysis ensuring the representation of large-scale circulation patterns.
We present a first evaluation of wildfire characteristics in the nudged CESM2 simulations by comparing simulated output with observational data. Specifically, we compare the simulations with observational data from GFED5 (Chen et al., 2023), to investigate mean values and trends in BA, fire season length and fire weather indices for the period 2001–2020. Further, we present a first assessment of historical BA trends, highlighting how circulation-driven variability and thermodynamic changes can be separated across regions.
This evaluation provides a foundation for future studies using nudged CESM2 simulations to represent key wildfire characteristics and enables attribution studies that disentangle the relative roles of changes in climate and land use between pre-industrial and historical periods.
How to cite: Eifler, L., Dunkl, I., Sippel, S., and Bastos, A.: From Observation to Attribution: nudged atmospheric circulation simulations to attribute burned area trends, EMS Annual Meeting 2026, Utrecht, Netherlands, 6–11 Sep 2026, EMS2026-158, https://doi.org/10.5194/ems2026-158, 2026.
In 2025, Türkiye experienced a series of compound climate extremes that led to significant cross-sectoral impacts. The year was recorded as the fifth-warmest in the last 55 years, with a mean temperature of 15.1°C, 1.2°C above the 1991-2020 normal. A new national absolute maximum temperature record was set in Silopi, located in the Southeastern Anatolia region, at 50.5°C. In parallel with these high temperatures, the country experienced its most severe meteorological drought since 1964, with annual precipitation 27.6% below the long-term average. This study uses a storyline approach to analyze how these atmospheric conditions created cascading risks for the agricultural sector. Meteorological data from 220 stations showed that 268 days of the year had positive temperature anomalies. Heatwave analysis revealed a marked increase in both frequency and duration compared to the 1991-2020 base period. While the historical average for heatwave duration is generally between 5 and 10 days, this period reached 34 days in Şırnak, situated near the southeastern border. Additionally, Tokat, located in the inner Black Sea transition zone, experienced the highest frequency with 5 separate heatwave events. These thermal extremes occurred alongside widespread water stress, as confirmed by Standardized Precipitation Index (SPI) and Percent of Normal Index (PNI) assessments, which showed varying levels of drought across nearly all regions. The combined effects of these record heatwaves and rainfall deficits led to production declines across primary crop categories. According to 2025 agricultural statistics, wheat and barley production decreased by 13.7% and 25.9%, respectively. Significant losses were also noted in industrial crops and resilient perennials such as olives, 34.7%, reflecting the severity of the moisture deficit. These findings show how localized temperature and precipitation extremes can cascade into national food security risks, highlighting the need for integrated, cross-sectoral adaptation strategies in the Eastern Mediterranean basin.
Keywords: Compound Extremes, Heatwave Frequency, Drought Monitoring, Crop Yield Loss, Eastern Mediterranean.
How to cite: Gurkan, H.: Compound Climate Extremes in Türkiye: A Storyline of Record-Breaking 2025 Heatwaves and Cascading Agricultural Impacts, EMS Annual Meeting 2026, Utrecht, Netherlands, 6–11 Sep 2026, EMS2026-450, https://doi.org/10.5194/ems2026-450, 2026.
Climate change increases the frequence and severity of extreme events (Planton et al., 2008 ; Goodess, 2013). Understand their impacts become then a priority for risk management and adaptation planning (Gkika et al., 2023). In this context the energy sector is more and more exposed to climate hazards, which can simultaneously affect supply (Jerez et al., 2015; Añel et al., 2017), demand (Staffell, Pfenninger, 2018), and infrastructure performance (Guddanti et al., 2024). This work focuses on assessing the impacts of extreme temperatures (heatwaves and cold spells) on energy systems in France. The aim is to identify systemic vulnerabilities under current and future climate conditions.To do so, we have developed a multi-approach framework to link climatic variables with power system outputs. This methodology enables the quantification of the exposure and sensitivity of electric networks to extreme weather conditions. Models of energy supply (nuclear, solar and wind) and demand used rely on weather variables as inputs, combine with installed capacity for supply estimation. We use climate analogues of the targeted event to construct factual and counterfactual, respectively affected and not affected by climate change. This allows to evaluate changes in climate variables and then to link these changes to energy metrics. To illustrate the framework, we present a case study of the August 2023 French heatwave. This event was characterized by extreme temperatures in southern regions due to a persistent heat dome, while the northern regions remained largely unaffected. On the electricity side, transmission outages were reported, highlighting the sensitivity of the grid and therefore positions it as a relevant example for a case study.The results of the analysis reveal climate change-driven impacts on both supply and demand. The extreme temperatures led to an increase in energy demand. These temperatures also affect nuclear efficiency, producing a decrease in nuclear availability. In addition, the lack of wind, decrease the wind production. Thus, the use of climate analogs provides a valuable tool for attributing changes in energy sector (here supply and demand) to climate change.
References
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A. Gkika et al. “Battling the Extreme: Lessons Learned from Weather-Induced Disasters on Electricity Distribution Networks and Climate Change Adaptation Strategies.” 2023.
C. Goodess, “How Is the Frequency, Location and Severity of Extreme Events Likely to Change up to 2060?” 2013
KP. Guddanti et al. “A Comprehensive Review: Impacts of Extreme Temperatures Due to Climate Change on Power Grid Infrastructure and Operation.” 2024
S. Jerez, et al. “The Impact of Climate Change on Photovoltaic Power Generation in Europe.” 2015.
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How to cite: Akakpo-Numado, A., Vrac, M., and Faranda, D.: Assessing the impacts of extreme temperature events on energy systems in France, EMS Annual Meeting 2026, Utrecht, Netherlands, 6–11 Sep 2026, EMS2026-557, https://doi.org/10.5194/ems2026-557, 2026.
Heat extremes are intensifying with climate change. However, coarsely resolved global climate models (like those from CMIP6) underestimate peak intensities and cannot capture spatial details of heat extremes. These limitations are particularly important at city scales, where population exposure and vulnerability are highest and local feedbacks can further intensify extremes. To support adaptation to heat extremes in cities, it is crucial to provide accurate local information. Therefore, we evaluate how heat extremes are represented in a global km-scale model, with a focus on the city scale. The latest km-scale climate models allow better resolution of small-scale processes and feedbacks, and improve the representation of land-surface heterogeneity at global scale. Therefore, they have the potential to improve the representation of urban heat extremes at scales important for impacts compared to established CMIP6 models. Here, we use the so-called “storyline simulations” from the IFS-FESOM model that reconstruct historical events by nudging the large-scale circulation to observations across three climate states: pre-industrial, present-day, and a future with 2°C warming. First, we evaluate heat extremes in km-scale storyline simulations against observations and reanalysis to establish their applicability. Then, we show the improved representation of heat extremes in km-scale resolution compared to the CMIP6 model resolution. We further quantify changes in the frequency, duration, and spatial extent of heat extremes on a global scale and for selected cities. We find that at the city scale, the added value of km-scale resolution is prominent. For example, during the 2021 heatwave in Athens, peak temperatures at CMIP6-like resolution are underestimated by 8.2°C compared to km-scale representation of the city center. At km-scale, the city is resolved into the city center and surrounding suburban areas, capturing higher local temperatures in the city center and showing spatial variability across the urban area. Our results demonstrate that global km-scale models improve the representation of heat extremes at city scales and allow for consistent analysis of urban heat extremes worldwide. This is relevant for city planners, showing that km-scale models have the potential to inform adaptation strategies under climate change.
How to cite: Županić, J., Brunner, L., and Sillmann, J.: The added value of global km-scale simulations for representing heat extremes at the city scale, EMS Annual Meeting 2026, Utrecht, Netherlands, 6–11 Sep 2026, EMS2026-293, https://doi.org/10.5194/ems2026-293, 2026.
Climate models are commonly evaluated based on their ability to reproduce global-mean warming, yet their fidelity in representing the three-dimensional (3-D) structure of atmospheric warming—and its implications for extreme event attribution—has received far less attention. Pseudo-global-warming (PGW) approaches implicitly assume that imposed warming perturbations realistically capture the observed vertical and horizontal temperature changes, an assumption that is rarely tested.
Here, we identify systematic, global-scale biases in the vertical and horizontal structure of atmospheric warming in leading CMIP6 climate models relative to observationally constrained reanalysis data. Despite accurately reproducing the integrated magnitude of warming, these models distort its three-dimensional structure. Using a storyline attribution framework, we show that these structural biases propagate into substantially different estimates of extreme rainfall intensification.
We illustrate this effect through high-resolution attribution simulations of the October 2024 Valencia storm (Spain) using the Model for Prediction Across Scales (MPAS). When simulations are forced with an observationally constrained warming signal rather than a CMIP6-derived one, the attributed increase in extreme rainfall roughly triples, driven by enhanced low-level moistening, increased convective instability, and stronger vertical wind shear. Our results further show that an additional warming of similar magnitude to that observed—plausible under future climate conditions—would lead to an increase in extreme rainfall of around 50%, highlighting the strongly nonlinear response of the system.
Together, our findings reveal a previously unrecognized structural source of uncertainty in attribution science and demonstrate that the 3-D structure of warming is a first-order control on extreme rainfall attribution, potentially leading to systematic underestimation of anthropogenic contributions in high-impact precipitation events.
How to cite: Insua Costa, D., Lemus Cánovas, M., Senande Rivera, M., M. H. Deman, V., L. Geirinhas, J., and G. Miralles, D.: Extreme rainfall attribution hinges on warming structure, EMS Annual Meeting 2026, Utrecht, Netherlands, 6–11 Sep 2026, EMS2026-520, https://doi.org/10.5194/ems2026-520, 2026.
Understanding the influence of climate change on environmental extremes is essential to inform adaptation and mitigation policies. Here, we employ an event-focused storyline methodology to evaluate changes in total precipitation linked to Storm Boris, which struck Central Europe in September 2024. Our study addresses both historical (attribution) and future (projection) changes, and builds on our previous work by exploring how results vary with the stringency of applied dynamical constraints. Simulations are conducted with the global CMIP6 coupled climate model AWI-CM1, in which the winds observed before and during Storm Boris—including the jet stream evolution—are imposed (nudged). Such simulations are performed with those same winds under a range of climate states: preindustrial, present-day, and possible future states with 2, 3, and 4 °C of global warming relative to preindustrial levels. We test two nudging regimes: (1) a "weak constraint," nudging only synoptic- and planetary-scale winds from ERA5 in the free troposphere to allow partial dynamical adaptation, and (2) a "strong constraint," imposing winds across all vertical levels and scales to fully inhibit dynamical changes.
Both approaches successfully represent the event, with the strongly constrained setup yielding higher present-day precipitation totals, yielding a present-day rainfall closest to observations. The intensification of accumulated rainfall from preindustrial to present-day is robust, showing increases of 7% (weak constraint) and 4% (strong constraint). For up to +3ºC of global warming, both methods display roughly linear increases in total rainfall. However, at +4ºC, the results diverge: under weak constraints, precipitation changes are minimal or slightly negative relative to present-day, whereas under strong constraints, they continue to increase linearly. These differences are due to thermally-induced dynamical adaptations allowed under the weak constraint. It remains unclear whether these responses represent actual physical responses or are influenced by methodological limitations, and whether similar divergence would be observed in other extreme events. Then, these discrepancies reinforce the need to study a broader set of events and to adopt multi-method approaches to project extreme precipitation changes.
How to cite: Sánchez Benítez, A., Athanase, M., and Goessling, H. F.: Storm Boris' rainfall: Robust increases at moderate warming levels, large uncertainty at higher warming, EMS Annual Meeting 2026, Utrecht, Netherlands, 6–11 Sep 2026, EMS2026-21, https://doi.org/10.5194/ems2026-21, 2026.
North Atlantic extratropical cyclones propagate towards Northern Europe and are one of the major natural hazards in mid-latitudes. The 2026 winter season witnessed an unprecedented number of named storms in southwestern Europe, resulting in fatalities and significant adverse impacts across the Iberian Peninsula. While windstorm Kristin was the most impactful event, featuring record-breaking observed wind gusts exceeding 200 km/h and disrupted power grid for weeks in Portugal among other damages, the sequence of subsequent cyclones throughout February, leading to saturated soils that resulted in severe flooding, exacerbated the disruption of critical infrastructure and transport networks.
As countries in southwestern Europe are not frequently affected by severe windstorms, a comprehensive assessment of the large-scale synoptic evolution and of the dynamical mechanisms that forced the unusual cyclone explosive development and sting jet associated with windstorm Kristin (27–28 January 2026) is presented, together with a description of the associated meteorological and socioeconomic adverse impacts. The study also considers the consecutive extratropical cyclones and their impact on the hydrological cycle of mainland Portugal. For this purpose, an objective lagrangean method, which identifies and follows individual lows, is applied for the assessment of the cyclone tracks and lifetime characteristics, which is complemented by the analysis of several thermohydrodynamical ERA5 reanalysis fields during the lifetime of the cyclones. A climatological analysis for the extended winter (October to April) on the period 1931-2026 is also performed, putting the 2026 winter season into perspective.
Recent studies document that windstorms affecting the Iberian Peninsula may have different characteristics and underlying mechanisms; they are not rare events, and their frequency of occurrence undergoes strong multidecadal variability. Research also highlights the complexity of rainfall patterns, and the need for disentangling natural variability from long-term trends in the region. By presenting other historical extreme winter seasons and high-impact events, this analysis will discuss how rare this season was and propose measures for improving awareness and preparedness, at the local scale, for high-impact climate events occurring in Portugal. This research is performed in the framework of project DouroRisk - prevention and mitigation of natural hazards at the local scale.
How to cite: Liberato, M. L. R.: Consecutive Extratropical Cyclones Impacting the Iberian Peninsula: A Comprehensive Analysis of the 2026 Winter Season, EMS Annual Meeting 2026, Utrecht, Netherlands, 6–11 Sep 2026, EMS2026-681, https://doi.org/10.5194/ems2026-681, 2026.
The Atlantic Meridional Overturning Circulation (AMOC) is an important feature of the Atlantic Ocean circulation transporting a large amount of heat into the North Atlantic (about 1.2 Pw at 26.5˚N), that strongly affects the Atlantic climate and its adjacent regions, including Western European and the Caribbean.
One of the possible tipping points induced by global warming is a collapse of the AMOC. Recent research suggests that an irreversible AMOC collapse might already start this century even under low emission scenarios1,2,3. Using state-of-the-art climate models of the latest IPCC report (CMIP6) we have investigated the impact of such an imminent AMOC collapse for the Caribbean climate for a low emission scenario. On average the time scale for a total collapse is more than a century. In the worst case resulting in a severe reduction in annual mean rainfall for specific regions in the Caribbean of about 70%. Other significant implications of an AMOC collapse are a reduction of the increase in surface temperatures in the order of one degree °C, an increase in sea level rise of about 10 cm, and less favourable conditions for hurricane activity. These findings quantify Caribbean climate change impacts in the event of an AMOC collapse, complementing existing climate scenarios.
References
- Ditlevsen, P., & Ditlevsen, S. (2023). Warning of a forthcoming collapse of the Atlantic meridional overturning circulation. Nature Communications, 14(1), 1-12.
- Drijfhout, S., Angevaare, J. R., Mecking, J., van Westen, R. M., & Rahmstorf, S. (2025). Shutdown of northern Atlantic overturning after 2100 following deep mixing collapse in CMIP6 projections. Environmental Research Letters, 20(9), 094062.
- Rahmstorf, S. (2024). Is the Atlantic overturning circulation approaching a tipping point? Oceanography, 37(3), 16-29.
How to cite: Haarsma, R., Keizer, I., de Vries, H., le Bars, D., Alinaghi, P., and Hiron, L.: Impacts of an AMOC collapse on the Caribbean Climate, EMS Annual Meeting 2026, Utrecht, Netherlands, 6–11 Sep 2026, EMS2026-532, https://doi.org/10.5194/ems2026-532, 2026.
The Arctic has undergone rapid climatic change over recent decades; however, the spatial variability of temperature extremes in complex coastal, tundra and glacial environments remains insufficiently quantified. This study investigates near-surface air temperature extremes in the Forlandsundet region (northwest Spitsbergen) over the period 2010–2015, based on year-round observations from six measurement sites representing distinct topoclimatic settings, including a mountain ridge, a glacier and its marginal zone, a moraine, a tundra, and a coastal zone.
Temperature extremes were identified using a station-specific, monthly percentile approach, allowing for the characterization of extremes relative to local climatic conditions and seasonal variability. Warm extremes were defined by the 90th, 95th, and 99th percentiles, while cold extremes were determined using the 10th, 5th, and 1st percentiles of mean daily air temperature. This method enabled a consistent assessment of both the frequency and intensity of extreme thermal events across contrasting environments.
The analysis focuses on spatial differentiation of extremes and on the role of topography, surface characteristics, and elevation in shaping local thermal regimes. Additionally, the influence of large-scale atmospheric circulation was examined using the Niedźwiedź classification for Svalbard, which enabled the identification of synoptic conditions associated with extreme temperature events. The role of local meteorological factors, including cloudiness and wind conditions, was also investigated.
The results provide a comprehensive assessment of temperature extremes across diverse Arctic topoclimates and highlight the combined influence of synoptic forcing and local environmental controls. These findings contribute to a better understanding of fine-scale climate variability in polar regions and offer valuable insights for improving Arctic regional climate modelling.
How to cite: Noroozian, R., Przybylak, R., Araźny, A., and Akbari Moghaddam Sani, S.: Near-surface air temperature extremes in the Forlandsundet region (northwest Spitsbergen, 2010–2015), EMS Annual Meeting 2026, Utrecht, Netherlands, 6–11 Sep 2026, EMS2026-216, https://doi.org/10.5194/ems2026-216, 2026.
The Indian Ocean Dipole (IOD) is an interannual air-sea coupled phenomenon in the tropical Indian Ocean that significantly influences global weather and climate. Accurate prediction of the IOD is therefore of great importance. However, the current skill of IOD forecasting is limited, partly due to inaccuracies in initial conditions. This study employs the coupled conditional nonlinear optimal perturbation (C-CNOP) method, which incorporates initial coupling uncertainties, to identify sensitive areas of targeted observations for positive Indian Ocean Dipole (IOD) events. Results show that the initial errors most likely to yield large prediction uncertainties of IOD events are mainly concentrated in sea temperatures near the thermocline in the eastern Indian Ocean (IO_Temp, 70-110m depth, 5°S-5°N, 85°E-105°E) and western Pacific (PO_Temp, 120-160m depth, 5°S-5°N, 130°E-150°E), as well as zonal winds (UWind), exhibiting an east–west dipole pattern over the tropical Indo-western Pacific. Through sensitivity experiments—designed to assess the impact of initial uncertainties in different areas on IOD predictions while bypassing the assimilation process and avoiding initial shock effects—we find that prediction uncertainties are more sensitive to initial errors in the UWind area than in the IO_Temp and PO_Temp areas, demonstrating a stronger impact on forecast skill, particularly in winter and summer. Further analysis demonstrated that the IO_Temp & PO_UWind (70-110m depth, 5°S-5°N, 85°E-105°E & zonal wind in the western Pacific) coupled area exhibits greater sensitivity than the UWind area alone, emerging as the most sensitive area of positive IOD events. This key area highlights both the Pacific's remote influence and the crucial role of local ocean on IOD development. These results underscore the critical role of coupled initialization in IOD predictability, offering a theoretical basis for advancing coupled data assimilation.
How to cite: Feng, R., Duan, W., and Hu, J.: Identifying Sensitive Areas for Targeted Observations to Improve Indian Ocean Dipole Predictions Using a Coupled CNOP Approach, EMS Annual Meeting 2026, Utrecht, Netherlands, 6–11 Sep 2026, EMS2026-628, https://doi.org/10.5194/ems2026-628, 2026.
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