The magnificent but complex topography of the Alps makes them Europe's indispensable water tower, channeling vital precipitation into rivers that sustain communities, economies, and ecosystems downstream. Therefore, gaining an understanding of how precipitation behaves in this mountain region is crucial. These mountain regions are home to a great variety of climatic regimes, from Mediterranean and Atlantic maritime to strongly continental, and yet, hydro-meteorological extremes pose an increasingly disastrous threat, heightening pressure on water security, infrastructure resilience, and transboundary risk management.

There has been much research into precipitation extremes that has taken place over various parts of the Alps, bringing to light their intensity, frequency, and dynamics. However, these studies are confined to specific regions of the mountain range. The novelty of this work is threefold: a) It is based on a consistent, high-resolution, observation-based, recently published alpine-wide database of High-Impact Precipitation Events (HIPEs), observed for the period 1961-2022 and developed through a homogeneous transnational approach (Lemus-Canovas et al., 2025); b) It is based on a multi-metric analysis to draw a comprehensive picture of changes in the HIPE features, including the assessment of their spatial extent, which has not been addressed before; and c) The variations in the different characteristics of these events, as well as the responsible drivers, are addressed by considering the contribution of the annual frequency of weather types.

By combining Regression Analysis with the modeling of the Generalized Extreme Value distribution, the study was able to identify significant patterns of increase in HIPE occurrences in subregions of the Alps, along with different contributions from the main weather types when explaining the annual variability of the different characteristics of HIPEs. Such techniques offer a nuanced view of the contribution of atmospheric dynamics and local factors in determining the observed variability of precipitation extremes.

Overall, our results indicate that the emerging shifts in HIPE behavior will most likely result from a nonlinear interaction of thermodynamic intensification and dynamics of circulation. The findings are intended to emphasize the emerging regional vulnerabilities and provide science-based support for cross-border preparedness for flooding and adaptation to climate change in Alpine environments.

Keywords: High-Impact Precipitation Events (HIPEs), European Alps, trend analysis, Alpine-wide observations

Reference: Lemus-Canovas, M. (2025). High-Impact Precipitation Events in the European Alps (1961–2022) [Dataset]. Zenodo. https://doi.org/10.5281/zenodo.17047822        

How to cite: Gagnani, B., Lemus i Cánovas, M., and Crespi, A.: Understanding the observed changes in High-Impact Precipitation Events across the European Alps, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-1064, https://doi.org/10.5194/egusphere-egu26-1064, 2026.

Extreme precipitation is a natural hazard with significant socioeconomic implications, namely for sectors such as agriculture, including viticulture. This study provides the first comprehensive analysis of extreme precipitation events in mainland Portugal, based on sub-hourly observations. Using 10-minute precipitation data from 71 weather stations for the period 2000 to 2022, we assess the spatial and temporal variability of these events, including their seasonality, diurnal cycle, and synoptic-scale drivers. The mean seasonal contribution of extreme precipitation to total annual precipitation, defined using thresholds of 10–20 mm h^-1 (yellow warnings) and >20 mm h^-1 (orange and red warnings) following the criteria of the Portuguese Weather Service, is highest in winter, indicating a stronger influence of intense precipitation on annual totals. This contribution decreases in autumn and spring, reaching its minimum in summer. Extreme precipitation events occur most frequently between September and December, with a secondary maximum in April and May, particularly in the Alentejo region. The diurnal cycle exhibits a pronounced afternoon peak, consistent with convectively driven thunderstorms. In spring and summer, extreme events tend to account for a larger fraction of daily precipitation totals. Two extreme events were selected not only as case studies of heavy precipitation, hail and lightning but also as examples of understanding the specific weather conditions and atmospheric dynamics associated with such severe weather patterns. In the first case, the event on 28 May 2018 in the Douro region was associated with a cut-off low, whereas in the second case, the event on 14 September 2021 in the Alentejo region was associated with a frontal system in the final phase of its life cycle. ERA5 instability indices show a good agreement with observed lightning patterns. These results, particularly at the regional scale, provide valuable insights for climate research and socio-economic sectors such as viticulture, where extreme precipitation and hailfall pose significant risks. Acknowledgements: Research funded by Vine & Wine Portugal–Driving Sustainable Growth Through Smart Innovation, PRR & NextGeneration EU, Agendas Mobilizadoras para a Reindustrialização, Contract Nb. C644866286-011. The authors acknowledge National Funds by FCT – Portuguese Foundation for Science and Technology, under the projects UID/04033/2025: Centre for the Research and Technology of Agro-Environmental and Biological Sciences (https://doi.org/10.54499/UID/04033/2025) and LA/P/0126/2020 (https://doi.org/10.54499/LA/P/0126/2020).

How to cite: Cruz, J., Belo-Pereira, M., Fonseca, A., and Santos, J. A.: Characterising Hourly Extreme Precipitation in Portugal: Spatial–Temporal Variability and Case Studies in Two Wine Regions, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-3539, https://doi.org/10.5194/egusphere-egu26-3539, 2026.

Estimating sub-daily precipitation return values at the kilometer-scale is critical for climate-risk assessments. However, this remains a challenge due to the strong intermittency of extremes, heterogeneous observational networks, and the breakdown of stationarity assumptions under climate change. Furthermore, coarse-resolution global climate models (e.g., CMIP6) systematically underestimate local extremes by smoothing convective processes and orographic gradients, thereby blurring hotspots that are critical for impact modelling.

We present an innovative statistical downscaling method that repurposes Areal Reduction Factors (ARFs), traditionally used to relate point rainfall to areal averages, as a resolution-bridging tool for extreme precipitation. By comparing return values derived from high-resolution (COMEPHORE, ~1 km2) and coarse-resolution (CERRA, ~25 km2) reanalyses, we compute spatially varying ARF maps. These maps quantify the attenuation of extremes induced by coarse spatial resolution and serve as multiplicative scaling factors to translate coarse-resolution outputs into 1-km products while preserving the large-scale climate signal.

This framework is validated against independent rain-gauge observations across multiple return periods and seasons. Finally, we apply the method to CMIP6 simulations to generate 3-hourly, 1-km precipitation return values for both historical and future periods.

This approach provides a computationally efficient and climate-change-consistent pathway to generate high-resolution hazard metrics, without the prohibitive cost of convection-permitting regional climate simulations.

How to cite: Droin, C., Lambert, A., Terrier, M., and Troin, M.: An ARF-based method to downscale sub-daily extreme precipitation return values, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-7863, https://doi.org/10.5194/egusphere-egu26-7863, 2026.

How to cite: Grieger, J., Ruff, F., Forster, C., Fauer, F., Feldmann, H., Fischer-Frenzel, P., Friederichs, P., Haufs, E., Lucio-Eceiza, E. E., Meredith, E. P., Merkes, S. T., Pinto, J. G., Schröter, J., Szemkus, S., Tonn, M., Ulbrich, U., Vlachopoulos, O., Voit, P., Vorogushyn, S., and Zimmermann, T.: ClimXtreme addressing heavy precipitation events, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-19066, https://doi.org/10.5194/egusphere-egu26-19066, 2026.

In recent years, the impact of extreme weather events, such as droughts, has been reported more frequently in Central and Eastern European countries. Rising temperatures and increasingly variable precipitation patterns affect natural ecosystems and agricultural areas, which could impact water resources, agricultural productivity and livelihoods in the future.

This study analyses the Standardised Precipitation Index (SPI), derived from Climate Hazards Centre Infrared Precipitation with Stations (CHIRPS) data, over the last 20 years. The regional focus is on Central and Eastern European countries, with a spatial gradient from the Baltic Sea to the Adriatic Sea in order to cover several biogeographic regions.

The SPI is a widely used tool for assessing the severity and frequency of drought events, providing a standardised measure of precipitation anomalies over various temporal and spatial scales. This study uses SPI-1 and SPI-3 data (at 1- and 3-month scales) for Poland, the Czech Republic, Slovakia, Slovenia, Hungary and Croatia between 2005 and 2024. The aim of analysing these data is to initially identify spatial and temporal patterns in drought frequency, duration, and intensity for this period.

Our future collaborative work will assess the relationship between analysed precipitation patterns and other key climatic factors (e.g. temperature) and further environmental factors (e.g. soil, water and vegetation conditions). This can provide a deeper understanding of the spatial and temporal distribution of climatic extreme events in this region. Furthermore, SPI analyses should be complemented by climate projections to provide insights into potential future changes in precipitation patterns and the frequency of extreme events in Central and Eastern Europe.

How to cite: Huth, J., Hordofa, A. T., Kropacek, J., Nagy, A., Habel, M., Mukanov, B., Maerker, M., and Bachofer, F.: Spatiotemporal variability of precipitation in Central and Eastern European countries: A 20-year time series analysis of CHIRPS data for the detection of extreme events, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-19630, https://doi.org/10.5194/egusphere-egu26-19630, 2026.

ClimXtreme is a research programme funded by the German Federal Ministry of Research, Technology and Space (BMFTR) that comprises 25 individual projects and aims to improve understanding of European extreme weather events and associated uncertainties under anthropogenic climate change. As part of the cross‑project collaboration, a coordinated approach is being established to initiate and sustain targeted stakeholder communication and support it throughout the project period. The aim is to develop information, data and tools targeted to stakeholder needs. To this end, Hazard‑specific Stakeholder Interaction (HaSSI) groups coordinate collaborative work on windstorms, heavy precipitation, and heat/drought.

The HaSSI Wind group includes projects investigating physical mechanisms behind cyclones (CyclEx), applying newly developed statistical methods (CoDEx and SCaHA), analyzing storm-related impacts (COO, FORTEC and ECCES II), and developing methods for knowledge exchange (ClimXchange). With the pressure tendency equation, project CyclEx quantifies the influence of diabatic heating on cyclone intensification. Cyclones with a relatively large diabatic heating influence exhibit steeper deepening rates, more warm conveyor belt activity, increased precipitation, and stronger wind gusts compared to cyclones with a small diabatic influence. Project CoDEx develops statistical models to estimate extreme values in space and time. Project SCaHA focuses on statistical modeling of clustering and seasonality in vorticity extremes over the North Atlantic using the fractional compound Poisson process, and on identifying suitable models for different regions. As for the storm-related impacts, project COO uses a tracking methodology to assign a loss motivated storm severity index to each storm event and to perform a loss motivated ranking of historical and future events. Project FORTEC uses logistic regression models to identify relevant storm damage factors and model current and future storm damage risk for two damage types: vegetation damage along railway lines and building damage. Project ECCES II uses hydrodynamic tide-surge model and sensitivity experiments to evaluate the impact of regional sea level rise on storm surges in the North Sea with a focus on the nonlinear processes. With respect to stakeholder engagement, project ClimXchange strengthens climate research communication by providing hands-on guidance on suitable approaches, methods and communication skills. With joint efforts, we aim to provide a diverse view regarding storms in Europe and exchange actively with relevant stakeholders.

How to cite: Liu, X., Lorenz, R., Grieger, J., Ulbrich, U., Friederichs, P., Pinto, J. G., Christ, S., Fischer-Frenzel, P., Fried, R., Mendel, M., Merkes, S. T., Quinting, J., and Zimmermann, T.: ClimXtreme addressing (large scale winter) storm events, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-19825, https://doi.org/10.5194/egusphere-egu26-19825, 2026.

As climate change intensifies, shifts in temperature and precipitation extremes raise concerns about increasing river flood risks. Understanding and quantifying how climate change influences floods is relevant from both theoretical and practical perspectives. From a research standpoint, although flood trend detection has been widely investigated, attributing observed changes to specific drivers remains challenging. From a practical perspective, reliable methods are needed to incorporate evolving climate conditions into flood predictions—particularly in ungauged basins—since traditional flood frequency analysis assumes stationarity.
In this work, we present research conducted at Politecnico di Torino addressing these issues. The objectives are to investigate the relationship between floods and climate extremes at both local and large spatial scales, and to propose methods for linking changes in flood frequency curves to evolving precipitation and temperature patterns. The study area is the Great Alpine Region (GAR), including the Po River valley in Northern Italy. Both data-based and modelling approaches are employed.
The data-based approach relies on the derived flood frequency framework and estimates flood sensitivity to changes in precipitation extremes by linking flood frequency curves and Intensity–Duration–Frequency curves through quantile–quantile relationships at the local scale. The modelling approach is based on the application of a regionally distributed rainfall–runoff model forced by climate model outputs, allowing changes in flood event characteristics to be investigated at the regional scale.
This study is carried out within the Clim2FlEx project and the RETURN Extended Partnership and is funded by the Italian Ministry of Education, Universities and Research (MUR, PRIN project 2022AX3882) and the European Union NextGenerationEU (National Recovery and Resilience Plan – NRRP, Mission 4, Component 2, Investment 1.3 – D.D. 1243 2/8/2022, PE0000005 – Spoke VS1).

How to cite: Viglione, A., Lombardo, L., Cafiero, L., Basso, A., Mazzoglio, P., Ganora, D., Claps, P., and Laio, F.: Linking climate and rainfall extremes to flood changes: data-based and modelling approaches, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-19843, https://doi.org/10.5194/egusphere-egu26-19843, 2026.

Flooding remains one of India’s most persistent hydroclimatic threats, driven by monsoon variability, rapid urbanisation, and chronic gaps in observational hydrometric data. These limitations affect both fast-growing cities and large river basins, where ungauged tributaries and limited socio-economic preparedness compound the overall risk. To address this challenge, we develop a holistic, multi-scale flood-risk assessment framework that integrates reanalysis-driven flood hazards with socio-hydrological vulnerability patterns across India. First, four leading Hydrological Reanalysis Datasets, ERA5, IMDAA, CFSR/CFSv2, and MERRA-2, are evaluated using three decades of rainfall. ERA5 emerges as the most reliable dataset across diverse hydro-climatic zones. Validation using CC, KGE, NSE, POD, FAR, EB, and CSI shows strong agreement with gauged data.  At the city scale, compound flood behaviour is quantified using bivariate and trivariate copulas linking long-duration rainfall (Rx1day), short cloudbursts (N25), and annual peak discharge (Q). River-connected cities exhibit pronounced upper-tail dependence under Gumbel–Hougaard structures, revealing synchronised extremes where intense rainfall and river overflow co-occur. Non-river cities show distinct rainfall-only compound signatures. These joint-probability structures provide realistic estimates of compound flood likelihoods in complex urban environments. At the basin scale, an extreme-value workflow using rolling annual maxima, KS-based distribution selection, and event-shape normalisation is applied to generate gridded 3-day, 5-day, and 7-day RP100 rainfall and discharge inputs. These time series force LISFLOOD-FP to produce representative design-flood hazard layers—depth, velocity, and depth×velocity—across India’s major basins. To capture the human dimension of flood impacts, a socio-hydrological module integrates 54 indicators of exposure, sensitivity, and adaptive capacity using a multi-criteria decision-making approach. District-level vulnerability scores and rankings reveal high- and very-high-vulnerability clusters across eastern, central, and northeastern India. These socio-economic patterns are analysed alongside hazard outputs, identifying critical “hotspot” regions where high exposure (population and area), weak coping capacity, and severe hydrodynamic hazards converge.

How to cite: Singh, H. and Mohanty, M. P.: Towards a Dual-Scale Flood Risk Assessment in India: Copula-Based Urban Extremes, Basin-Scale Design Flood Simulation, and Socio-Hydrological Vulnerability, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-1115, https://doi.org/10.5194/egusphere-egu26-1115, 2026.

In Central Europe, climate change contributes to an increasing frequency of devastating flood events, such as those observed in Western Europe in July 2021. While floods with return periods of up to 200 years have been relatively well studied, understanding and preparedness for more extreme, less frequent High-Impact-Low-Probability (HILP) floods remain limited. A key tool to assess the potential consequences of such events is the stress-test scenario. More specifically, these are hypothetical yet plausible simulations of a very low-likelihood flood events.

This review systematically analyses scientific studies that apply stress-testing approaches to HILP floods. The focus lies on research examining the impacts of very extreme pluvial and fluvial floods on humans, the built environment, and critical infrastructure. A systematic SCOPUS keyword search initially identified ~12,000 studies, which were reduced to 137 relevant publications using a filtering process assisted by a large language model. The selected studies are differentiated by how physical boundary conditions are derived, floods are modeled, and impacts are quantified.

The analysis shows that most studies use univariate statistical methods to derive hypothetical rainfall events, while more complex approaches such as climate model reforecasts or multivariate weather generators are employed far less frequently. A wide range of techniques is used to modify historical events to simulate unprecedented flooding. Counterfactual scenarios in flood modeling mainly investigate the effects of reservoirs and similar structures, whereas other simulations explore the potential of early-warning systems to reduce exposure. In terms of impact modeling, the reviewed literature examines a broad range of system components. About 60% of studies employ simple GIS overlays to assess the number of structures affected by floodwaters, while more advanced modeling tools include agent-based models, cascading impact models, network theory, and multi-criteria decision models. Only a few studies assess multi-sectoral impacts, and their analyses are often shallow or overly simplified. Future research should address this gap to achieve a more comprehensive understanding of the potential damages caused by HILP floods.

How to cite: Lennartz, M., Vorogushyn, S., and Merz, B.: Stress testing approaches for High-Impact-Low-Probability floods: A review, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-1846, https://doi.org/10.5194/egusphere-egu26-1846, 2026.

Climate change is expected to alter floods in complex ways. Understanding changes in flood frequency and driving processes under changing climatic conditions is urgently needed to develop sustainable adaptation measures to floods in rivers such as the Swiss section of the Rhône, which has been hit by a severe flood in June 2024. The investigation of future flood characteristics and driving processes is, however, challenging because these events exhibit large interannual variability and are, among other processes, caused by localized intense precipitation, which is not optimally represented in global climate model simulations. High-resolution initial condition large ensembles and convection-resolving climate simulations can be used to address these challenges. This work leverages these two types of climate ensembles and process-based hydrologic modeling to quantify long-term changes in peak flows in the Swiss section of the Rhône until the end of the century, and their uncertainty. We will use these simulations to investigate the driving processes underlying future floods and how these differ from those relevant for flood generation today. In doing so, we provide information crucial for decision-making related to future flood protection and adaptation.

How to cite: Elkouk, A., Astagneau, P. C., Wood, R. R., and Brunner, M. I.: Improving flood projections in the Rhône River using high-resolution initial condition large ensembles and convection-resolving climate simulations, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-4837, https://doi.org/10.5194/egusphere-egu26-4837, 2026.

The Spanish Mediterranean region is frequently impacted by flash floods, driven by intense convective rainfall, the presence of torrential basins, and dense urbanization in flood-prone areas. This situation can be aggravated by climate change, as demonstrated in recent studies. In this context, one way to reduce risk is to decrease vulnerability by improving both early warning systems and public preparedness. The recently completed Next Generation Flood2Now project aimed to address these two points through an interdisciplinary approach that brought together experts in hydrology, meteorology, sociology, databases, and complex systems, complemented by the participation of a private company. Two basins that suffer frequent flooding were chosen for the project: the torrential basin of the Francolí River, which can be dry in some reaches and times of the year but often experiences catastrophic flash floods as a result of intense rainfall, and the Arga River basin, characterized by a permanent flow produced by snowmelt and rainfall.

The ultimate goal of the project was to develop a hydrometeorological prediction chain that can be used operationally to aid decision-making in the face of potential floods. This was achieved using the INUNGAMA and PIRAGUA_flood (Llasat et al., 2024) databases, which contain all recorded floods in Catalonia and the Pyrenees, respectively, between 1980 and 2020. Based on these databases, an analogue model was developed (Guzzón et al., 2025) that considers the geopotential fields of 1000 and 500 hPa and weather types classified according to the method of Beck et al. (2007). In parallel, the RIBS hydrological model (Garrote and Bras, 1995) was calibrated at a set of points in the respective basins, where streamflow data recorded at gauging station are available, and subsequently fed with rainfall fields corresponding to flood events and their analogues, generating a probabilistic flow output that allows estimating the uncertainty of a given flood event causing damage (Carril-Rojas et al., 2025). For this purpose, flood compensation payments were taken into account, based on information from the Insurance Compensation Consortium. The data generated by the analogues, as well as the predictions obtained from the GFS, constituted the input for the Delft-FEWS platform (https://oss.deltares.nl/web/delft-fews/), which generates a set of flow rates and an exceedance warning as output. To update the flood databases, an AI-based methodology was created that extracts information from the press, analyzes it, and inputs it into the database. At the same time, citizen science campaigns, workshops and exhibitions have been developed, both to raise awareness among the population in both basins and to obtain more real-time observations of the river level. The contribution presented here shows the methodological synthesis of the project and the main results.

Carril-Rojas, et al., 2025. A Flood Forecasting Method in the Francolí River Basin (Spain) Using a Distributed Hydrological Model and an Analog-Based Precipitation Forecast. Hydrology, https://doi.org/10.3390/hydrology12080220

Garrote, L.; Bras, R.L., 1995. A Distributed Model for Real-Time Flood Forecasting Using Digital Elevation Models. JoH, https://doi.org/10.1016/0022-1694(94)02592-Y

Llasat, M.C., et al., 2024. Floods in the Pyrenees: A global view through a regional database, NHESS, https://doi.org/10.5194/nhess-24-3423-2024

How to cite: Llasat, M.-C., Marcos Matamoros, R., Guzzon, C., Arbaizar, J., Cabañas, A., Cachay Melly, J., Carril-Rojas, D., Díaz Guilera, A., Fernández-Fidalgo, J., Garrote, L., Llasat-Botija, M., Marinelli, D., Mediero, L., and Varela, O.: Improvement of early warning systems for flood risk with a distributed hydrological model and an analog-based precipitation forecast, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-8029, https://doi.org/10.5194/egusphere-egu26-8029, 2026.

To assess flash flood susceptibility, an analysis of physiographic parameters of catchments affected by flash floods in the past was conducted. The study focuses on 17 catchments, ranging from 9 to 80 km², with water gauge stations in the Czech Republic. Based on parameters such as catchment area, slope, elevation, Melton ratio, river length and slope, river network length and density, shape index, arable land proportion, curve number, and road network density, categorization into clusters I–III was performed using principal component analysis and k-medoids clustering. To evaluate the hydrological response in relation to these parameters, the flashiness index (quantifying the magnitude and timing of the flood wave) was calculated for events in which peak discharge exceeded the 1-year return period discharge. The results show that the highest flashiness values were recorded in a group of small, steep catchments characterized by high terrain roughness, maximum elevations, a dense river network, and compact shape.

How to cite: Honzíčková, D., Šulc Michalková, M., Borga, M., Brázdil, R., Štěpánek, P., Zahradníček, P., Coufal, P., Geršlová, Z., and Caletka, M.: Why do some catchments exhibit a more flashy response? Catchment parameters controlling flash flood generation., EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-11176, https://doi.org/10.5194/egusphere-egu26-11176, 2026.

Flooding is one of the most severe natural hazards worldwide, causing substantial economic losses and catastrophic impacts. Although levees are widely implemented for flood mitigation, few global flood models explicitly incorporate their influence on flood routing and risk assessment. In our previous study (Zhao et al., 2025, doi: https://doi.org/10.1029/2024WR039790), we developed a levee module for the CaMa-Flood model and generated levee parameters for global ungauged rivers. Furthermore, recent GPU-based computational optimizations in CaMa-Flood model (Kang et al., 2026, doi: 10.22541/essoar.176442648.85093032/v3) now enable simulations of global flood dynamics at high spatial resolution within a feasible timeframe.

These advancements allow us to analyze long-term changes in flood hazards and risks while explicitly accounting for levee effects. In this study, we forced the CaMa-Flood model with ensemble CMIP6 runoff data to simulate historical and future river hydrodynamic changes at the daily scale, comparing scenarios with and without levee considerations. Our results reveal that levees significantly mitigate global flood hazard. Driven by this protection, urbanization rates within levee-protected areas have substantially outpaced those in unprotected regions over the past four decades. Conversely, flood hazard in unprotected river reaches increases due to the hydrodynamic effects induced by levee construction. Regarding flood exposure, multi-model estimates show that while levees have historically played a crucial role in shielding a substantial fraction of the floodplain population and slowing the growth rate of flood exposure, rapid population growth has largely offset these protective benefits. Consequently, the absolute flood-affected population under levee protection in the present day remains at a historically high level, comparable to that of previous decades without levees. Furthermore, projections indicate that flood exposure will continue to increase through the mid-21st century under changing climate conditions. Overall, this work provides new insights into global flood modeling and risk assessment and supports improved flood management and decision-making in levee-protected regions.

How to cite: Zhao, G., Yamazaki, D., Hirabayashi, Y., Khanh, D. N., and Kang, S.: New Insights Into Global Flood Hazard and Risk Considering Levees Under a Changing Climate, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-15543, https://doi.org/10.5194/egusphere-egu26-15543, 2026.

Climate change is expected to alter the frequency, magnitude, and temporal structure of flood waves, with direct implications for local scour development at bridge piers and, consequently, for bridge safety and management. While numerous studies have addressed individual methodological components of this problem - such as climate change projections, hydrological modelling, or scour estimation - the methodological links between climate change indicators, flood wave characteristics, and local scour processes remain fragmented and are often treated in isolation. This contribution presents preliminary insights from an ongoing systematic literature review that investigates how climate change signals are propagated through hydrological and hydraulic modelling chains and ultimately reflected in local scour assessments at bridge piers. The review focuses on peer-reviewed studies addressing (i) climate change modelling and approaches, (ii) hydrological representations of flood waves, including peak flow, volume, duration, and hydrograph shape, and (iii) deterministic and probabilistic methods for evaluating local scour. Attention is given to how uncertainties are treated across these methodological steps and to the extent to which flood wave characteristics beyond peak discharge are explicitly considered. The preliminary synthesis highlights recurring methodological patterns, key knowledge gaps, and inconsistencies in current practice, especially regarding the integration of climate projections with hydrological design events and scour modelling frameworks. The findings are organized into a structured classification of modelling approaches, input data requirements, and uncertainty treatment, providing a basis for further in-depth analysis. This contribution provides a structured synthesis of existing methodological approaches and identifying gaps relevant for future model development and application of climate change assessment on erosion processes around bridge piers.

Acknowledgment:

This work has been supported in part by the Croatian Science Foundation under the project SERIOUS – Synthetic dEsign hydrographs undeR current and future clImate for local bridge scOUr aSsessment (IP-2024-05-1497) and by the “Young Researchers’ Career Development Project – Training New Doctoral Students” (DOK-2020-01-5354).

How to cite: Potočki, K., Panchanathan, A., Lacko, M., and Bezak, N.: Climate Change, Flood Wave Characteristics and Local Scour: A review of Modelling Approaches, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-16359, https://doi.org/10.5194/egusphere-egu26-16359, 2026.

Future fluvial flood risk can be estimated by applying change factors (CFs) to present day fluvial flood hydrology. At the global scale, CFs can be derived from the outputs of GCM-GHM ensemble pairs, such as ISIMIP3b. CFs are often calculated based on the proportional change of an index flood (e.g. median annual flood) between the future and present day, and subsequently applied over all return periods. Whilst this approach is statistically robust given the limited number of samples in ISIMIP3b, it assumes that rare and regular floods change proportionally. Scientific literature disputes this, suggesting that future changes may vary significantly based on flood rarity. Calculation of return period specific change factors can be achieved with stationary extreme value analysis on baseline and future periods, however the limited samples from climate ensembles result in noisy change factors for extreme events. Moreover, the analysis applied using different ensemble members results in greater uncertainty. A potential method to increase sample size to reduce noise is to apply non-stationary extreme value analysis across an ensemble’s entire time series.

This work compares multiple approaches to derive return period dependent CFs, including stationary flood frequency analysis, and non-stationary GAMLSS, across multiple distributions and ensemble members. We demonstrate and assess the differences in CFs between 2y (regular) and 100y (rare) floods, using Europe as a test domain. We find that CFs can be modeled as return period dependent, with clear spatial patterns present, and significant differences in changes in rare and regular floods in many locations. The variation of CF with return period depends on region, driving GCM - GHM pair, and the selected distribution fitting approach. In some locations, GCM-GHM pairs largely agree on the degree and direction of change between rare and regular floods, yet in others, GCMs-GHM pairs can give very different estimates of this (including opposing directions). Stationary flood frequency analysis for CFs results in greater noise, and CFs with greater magnitude. The GAMLSS approach significantly mitigates spatial noise, but reduces the intensity of changes in CFs as a function of RP. Our study overall highlights the importance of considering how CFs vary by return period, and how this variation itself is critically dependent on the driving GCM-GHM ensemble used.

How to cite: Hatchard, S. and Addor, N.: Do Small and Large River Floods Change Differently under Future Climate Change? , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-17320, https://doi.org/10.5194/egusphere-egu26-17320, 2026.

ABSTRACT

Compound drought–heat events are increasing under climate change, yet quantitative assessment of how antecedent drought alters extreme heat risk remains limited. This study examines the relationship between antecedent moisture conditions and extreme summer temperatures in Korea by combining correlation analysis with copula-based probabilistic modeling. Spearman’s rank correlation analysis reveals a consistent negative association between antecedent Standardized Precipitation Index (SPI) and daily maximum temperature (Tmax), indicating that drier conditions are systematically linked to higher summer temperatures. Copula models are then used to characterize the joint dependence between SPI and Tmax and to estimate conditional exceedance probabilities and return periods under different moisture states. The results show that dry and extremely dry conditions increase the probability of exceeding high Tmax thresholds and substantially shorten return periods, whereas wet conditions suppress extreme heat risk. The influence of antecedent drought becomes more pronounced for longer return periods, highlighting enhanced sensitivity in the extreme tail of the temperature distribution. These findings suggest that extreme heat risk is dynamically conditioned by prior hydrological states, emphasizing the importance of accounting for antecedent drought in interpreting and anticipating high-impact temperature extremes.

Acknowledgments

This work was supported by Korea Environment Industry & Technology Institute(KEITI) through Research and Development on the Technology for Securing the Water Resources Stability in Response to Future Change Project, funded by Korea Ministry of Climate, Energy, Environment(MCEE)(RS-2024-00332300) and by the National Research Foundation of Korea(NRF) grant funded by the Korea government(MSIT)(RS-2025-00563294).

How to cite: Lee, J., Jung, H., Kim, H., and Kim, S.: Analysis of the Impact of Preceding Drought on Heatwave Extremes: Focusing on South Korea., EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-9019, https://doi.org/10.5194/egusphere-egu26-9019, 2026.

ClimXtreme is a research programme funded by the German Federal Ministry of Research, Technology and Space (BMFTR) that comprises 25 individual projects and aims to improve understanding of European extreme weather events and associated uncertainties under anthropogenic climate change. 

As part of the cross‑project collaboration, a coordinated approach is being established to initiate and sustain targeted stakeholder communication and support it throughout the project period. The aim is to develop information, data and tools targeted to stakeholder needs. To this end, Hazard‑specific Stakeholder Interaction (HaSSi) groups coordinate collaborative work on windstorms, heavy precipitation, and heat/drought. This contribution summarizes activities within the HaSSi Heat/Drought group and provides an overview of the activities and outcomes within the project. 

Our group brings together diverse projects that study heatwaves and droughts from multiple perspectives. These include their underlying large‑scale dynamics, their impacts (for example on crops and fire weather), and event‑based attribution methods. The combination of expertise from various fields gives us a multifaceted perspective on heat and drought events, which we use in two ways:

Communication with stakeholders is maintained through monthly online seminars, in which scientific findings and their relevance to stakeholders are discussed, thereby gathering valuable input from the stakeholders' perspectives. As valuable, cross-project output tailored to stakeholders, we also analyse current extreme events from the multifaceted view gained from the respective individual projects. We illustrate this approach using findings from the ClimXtreme report on the European Summer 2025.

How to cite: Lemburg, A., Szemkus, S., Buschow, S., Dietz, V., Dillerup, I., Feldmann, H., Fischer-Frenzel, P., Friederichs, P., Grieger, J., Kraulich, F., León-FonFay, D., Merkes, S. T., Pfleiderer, P., Pinto, J. G., Schröter, J., Sippel, S., Ulbrich, U., Vlachopoulos, O., and Zimmermann, T.: ClimXtreme addressing heat and drought events, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-19526, https://doi.org/10.5194/egusphere-egu26-19526, 2026.

Poland’s location in the mid-latitudes determines a moderate influx of solar radiation and characteristic circulation conditions that strongly control weather variability. Of particular importance is the dominance of westerly circulation, enhanced by the zonal configuration of relief. The frequent passage of cyclones and atmospheric fronts, together with the advection of air masses with highly contrasting thermal properties, results in pronounced weather variability. Depending on the direction of air-mass advection, both extremely warm and extremely cold conditions may occur.

Since the late 20th century, the climate has been characterized by the predominance of anomalously warm months, seasons, and years. Notable examples include the exceptionally hot summer of 2003 in Western Europe and the summer of 2010 in Eastern Europe, both of which caused thousands of excess deaths among populations unaccustomed to prolonged heat stress. At the same time, extreme cold events still occur. Although they have become less frequent and less intense than during the 20th century, they continue to generate severe economic and biometeorological impacts. An example is January 2017 in the Balkan Peninsula, which was among the coldest and snowiest months on record in that region.

In the current year, a pronounced negative temperature anomaly was also observed: in May, snowfall and widespread frost occurred over large parts of Poland, as widely reported by the media. The aim of this study was therefore to assess how strongly thermal conditions in May 2025 deviated from the long-term climatological mean in Poland. Specifically, the study seeks to quantify the magnitude of the air-temperature anomaly and to identify the synoptic conditions responsible for the persistence of anomalously low temperatures.

The primary dataset consists of mean monthly air temperatures for May for the period 1951–2025 from 60 synoptic stations in Poland. These publicly available data were obtained from the database of the Polish national meteorological service (IMGW-PIB, https://danepubliczne.imgw.pl/). This dataset was used to calculate the magnitude of the temperature anomaly in May 2025. Anomalies were expressed both in absolute terms (°C) and in standardized units (multiples of the standard deviation, SD). A second dataset, also obtained from IMGW-PIB, comprises daily mean, maximum, and minimum air temperatures for May for the 75-year period 1951–2025 from selected stations. Synoptic conditions were analysed using surface and upper-air weather charts from the Polish (meteo.imgw.pl) and German (www.wetter3.de) meteorological services.

Preliminary results indicate that the cold conditions over Poland were primarily controlled by low-pressure systems that induced advection of cold air from the northern sector. Their persistence was favoured by a characteristic omega-blocking pattern over Western Europe and the presence of a deep upper-level trough over Central Europe. This configuration effectively inhibited the eastward progression of baric systems, allowing the prevailing weather regime to persist over the region for an extended period.

How to cite: Guzik, I. and Twardosz, R.: Global warming vs local reality: Poland's exceptionally cool May 2025, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-13998, https://doi.org/10.5194/egusphere-egu26-13998, 2026.

Climate change is altering the frequency, intensity, and spatial patterns of extreme weather events. Urban areas are particularly affected because population, critical infrastructure, and economic assets are concentrated in a relatively small area, which increases the potential impacts of extreme events. Climate risk depends not only on the hazard itself, but also on what is exposed and how sensitive it is to the damage. Understanding climate risk requires considering how hazards interact with exposure and vulnerability. For this reason, risk-based approaches are increasingly needed to support climate adaptation and decision-making. The increasing availability of open-source tools, including the CLIMADA model, has made climate risk assessment more accessible. This study applies the CLIMADA risk assessment framework to evaluate urban heat-stress risk in Vilnius, Lithuania.

The analysis focuses on heat stress as an illustrative case to examine potential impacts on human health under three levels of global warming: the recent past, +2 °C, and +4 °C relative to pre-industrial conditions. Climate hazards are characterised using projections from climate models, and uncertainty in future conditions is represented through an ensemble of simulations. This ensemble consists of five CMIP6 models: CanESM5, ACCESS CM2, GFDL-CM4, MPI-ESM1-2-LR, and NorESM2-MM. Heat-stress intensity affecting people is quantified using the Humidex index, which is calculated from gridded air temperature and relative humidity data and used as the hazard intensity in the risk assessment. Exposure is defined as the spatial distribution of people and relevant assets. Because this study focuses on heat-stress risks to human health in urban areas, exposure is characterised using a set of socio-demographic and urban indicators, including population distribution, age structure, the locations of critical infrastructure, and urban surface characteristics. Vulnerability describes how sensitive exposed people or assets are to the hazard and is represented through vulnerability functions. In this analysis, vulnerability is expressed through the mean damage degree and the percentage of affected assets, which are used together to estimate the mean damage ratio.

Preliminary results are presented as risk maps showing the spatial distribution of heat-related health risk in Vilnius under different climate change scenarios. This spatial information supports the identification of priority areas for adaptation planning and risk reduction.

How to cite: Kapilovaite, J.: Assessing Urban Heat-Stress Risk Under Future Climate Scenarios: A Case Study of Vilnius, Lithuania, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-15466, https://doi.org/10.5194/egusphere-egu26-15466, 2026.

This paper presents a comparison of three methods for determining cumulonimbus (Cb) cloud-top heights. Two methods are prognostic, based on the 1/4 CAPE and 1/2 CAPE approaches, while the third is a diagnostic polynomial method. The analysis was conducted for five convective events that developed under different synoptic and mesoscale conditions. Surface and upper-air synoptic charts, WRF simulations, radar, and radiosonde data, as well as observed lightning data are used to characterize the convective environments and evaluate the methods. The polynomial method, which approximates moist adiabats using a fifth- degree polynomial function, proved to be successful in diagnosing the Cb cloud-top heights at the observed locations. Therefore, cloud-top heights obtained from the polynomial method are used as a reference values for assessing the performance of the prognostic methods. Results show that, for most locations and events, both CAPE-based methods tend to overestimate Cb cloud-top heights. The 1/4 CAPE method generally exhibits smaller deviations from the observed values of Cb cloud-top heights than the 1/2 CAPE method. Nevertheless, the Cb cloud-top heights estimated using both CAPE methods exhibit a strong linear correlation with observed heights in four out of five analyzed events.

How to cite: Oštrić, S., Šoljan, V., Babić, K., and Telišman Prtenjak, M.: Comparison of Methods for EstimatingThunderstorm Cloud-Top Heights, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-20647, https://doi.org/10.5194/egusphere-egu26-20647, 2026.

Hailstorms are a major source of insured losses in Europe, with unprecedented damage in France in 2022 exceeding €6 billion. Anticipating how hail frequency may evolve under climate change is therefore critical for insurance risk management. This study assesses future changes in atmospheric environments conducive to hail using EURO‑CORDEX regional projections based on CMIP5 simulations. Sixteen GCM–RCM pairs provide 6‑hourly atmospheric fields at 11 km resolution, enabling a temporal and spatial high‑resolution, multi‑model analysis. Following the approach of Rädler et al. (2018), we compute key convective indices - Lifted Index (LI), 0-6 km vertical wind shear, and accumulated precipitation - and flag hail‑conducive days as a proxy for hail occurrence. Over the historical baseline (1980–2005), France experienced approximately 75 hail-conducive days per season (April–October). By mid-century (2050), the multi-model mean projects ~95 days, corresponding to a ~26% increase, rising to ~40% by 2100 under RCP8.5. The upward trend is statistically significant; however, substantial spatial heterogeneity is observed across France and adjacent countries. These findings have direct implications for the insurance sector: increasing hail risk challenges current pricing models, portfolio management, reinsurance treaty, and underscores the necessity of integrating climate projections into catastrophe modeling frameworks.

Keywords: hailstorms; severe convective storms; EURO‑CORDEX; CMIP5; RCP8.5; Lifted Index; vertical wind shear (0–6 km); multi‑model ensemble; regional climate modeling; France; insurance risk.

How to cite: Romain, M., Gilles, A., and Blandine, L.: Hailstorms in a warming climate: What future for France and insurance sector?, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-6709, https://doi.org/10.5194/egusphere-egu26-6709, 2026.

An increasing frequency of severe weather events, including damaging winds and tornadoes, poses significant hazards to infrastructure, ecosystems, and society (Fischer et al., 2025). Accurate and rapid surveys of damage caused by severe convective wind events remain challenging, yet they are essential for understanding impacts, refining wind-damage classification schemes, and improving severe weather databases. This study demonstrates an integrated workflow that combines a GIS-based mobile field-mapping application with multi-scale remote sensing data (UAV imagery and high-resolution satellite observations) to document and assess severe wind damage using the new International Fujita Scale (ESSL 2023). The approach is demonstrated through a case study of a tornado and associated severe wind damage near Hažlín in eastern Slovakia. A configurable mobile mapping platform tailored for damage surveys was used by the field team to collect standardized, georeferenced damage observations, damage type and intensity, and photographic documentation. Complementing the field mapping, high-resolution UAV surveys and pre-/post-event satellite imagery supported detailed characterization of vegetation and structural damage patterns at spatial scales unattainable from ground surveys alone. By integrating standardized field observations with remote sensing, the proposed approach improves damage classification accuracy and contributes to severe-wind climatological databases.

ESSL: International Fujita (IF) Scale, https://www.essl.org/cms/research-projects/international-fujita-scale/ (last access: 12 January 2025), 2023.

Fischer, J., Groenemeijer, P., Holzer, A., Feldmann, M., Schröer, K., Battaglioli, F., Schielicke, L., Púčik, T., Antonescu, B., Gatzen, C., and TIM Partners: Invited perspectives: Thunderstorm intensification from mountains to plains, Nat. Hazards Earth Syst. Sci., 25, 2629–2656, https://doi.org/10.5194/nhess-25-2629-2025, 2025.

How to cite: Fedor, T., Nováková, M., and Onačillová, K.: Damage assessment of severe wind events using mobile field mapping and remote sensing: A case study of the Hažlín tornado, Slovakia, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-11984, https://doi.org/10.5194/egusphere-egu26-11984, 2026.

Windstorm events are among the most damaging weather-related hazards in Europe, accounting for approximately 70% of the total insured losses. Existing research has primarily focused on storm climatology, hazard metrics, and loss-based assessments derived from insurance or damage data. While these approaches have advanced our understanding of windstorm dynamics and impacts, they provide limited insights into the spatial and quantitative characteristics of exposure.

Windstorm damage occurs where localised extreme wind gusts intersect with exposed socio-environmental assets. Exposure is therefore a central, yet still insufficient quantified, component of windstorm risk. In practice, exposure is often approximated through loss proxies or simplified indicators, largely due to persistent challenges in defining spatially coherent storm-affected areas from gust observations at the event scale.

This study develops an event-based windstorm exposure analysis for Sweden by linking observationally identified windstorm events with high-resolution national exposure datasets. Windstorms are identified from long-term station observations using local percentile exceedances combined with spatio-temporal clustering, resulting in a catalogue of extreme events. Storm-affected areas are identified using a single exceedance-based footprint approach applied to two wind data sources: interpolated station observations and ERA₅ reanalysis. In both cases, footprints are defined using locally derived percentile thresholds of wind gust intensity, capturing areas that experienced unusually strong winds relative to  local conditions.

For each event and footprint definition, exposure is quantified through the spatial intersection of extreme winds with datasets that describe population distribution, buildings, transportation, forest structure, and topography. Exposure metrics include population counts, infrastructure length, building numbers, and forest area.

A comparative analysis of exposure estimates across different footprint methodologies is conducted to assess the sensitivity of windstorm exposure to footprint definition. The resulting dataset provides an event-based representation of windstorm exposure in Sweden and establishes a foundation for improved attribution of impacts and future vulnerability and risk analyses.

How to cite: Georgali, E. and Karagiorgos, K.: Event-based windstorm exposure in Sweden using observational and reanalysis-derived storm footprints., EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-20762, https://doi.org/10.5194/egusphere-egu26-20762, 2026.