National climate change assessments and scenarios have become an essential requirement for decision-making at international, national and sub-national levels. Over recent years, many European countries have set up quasi-operational climate services informing on the current and future state of the climate in the respective country on a regular basis (e.g. KNMI'23 in the Netherlands, UKCP in the UK, CH2018 and CH2025 in Switzerland, ÖKS15 and ÖKS26 in Austria, National and federal states Climate Reports in Germany). However, the underpinning science to generate actionable climate information in a user-tailored approach differs from country to country. This session aims at an international exchange on these challenges focusing on:
- Practical challenges and best practices in developing national, regional and global climate projections and predictions to support adaptation action and impact assessments.
- Developments in dynamical and statistical downscaling techniques, process-based model evaluations, AI techniques and quality assessments.
- Methods to quantify uncertainties from climate model ensembles, combination of climate predictions and projections to provide seamless user information.
- Examples of tailoring information for climate impacts and risk assessments to support decision-making and demonstration on evaluation steps taken to monitor the uptake of climate information.
The Climate Evaluation and Quality Assessment for Reliability (CL3AR) is DWD's comprehensive and integrated quality assessment framework for regional climate simulations. As the demand for reliable high-resolution climate data grows, CL3AR serves as a critical bridge between raw model output and actionable climate services. The framework employs a multi-scale approach that evaluates model performance across a hierarchical spectrum: it combines the assessment of global teleconnections with the analysis of European-scale synoptic circulation patterns and fine-scale regional similarity metrics for key climate variables.
In this study, results from an ensemble of more than 20 regional climate simulations produced in the EURO-CORDEX framework are presented. To ensure a robust validation, model outputs are compared against a variety of high-quality reference data, including global and regional reanalysis datasets as well as high-resolution gridded observations. The evaluation focuses on a suite of essential climate variables critical for impact modeling and climate adaptation, specifically 2m temperature, precipitation, surface wind speed, and global shortwave radiation. By analyzing these variables, CL3AR identifies model strengths and systematic biases.
Following the evaluation phase, the framework incorporates a post-processing pipeline where bias-adjustment and further statistical downscaling are performed. These steps are essential to fulfill the requirements of stakeholders within the German domain, extending to the hydrological scale by including the transboundary river catchments of neighboring countries. Ultimately, CL3AR provides consistent, quality-controlled reference ensembles based on the latest EURO-CORDEX CMIP6 simulations. These ensembles serve as a foundational dataset for climate adaptation strategies, urban planning, and water management in Germany, ensuring that local decisions are supported by the highest standard of regional climate science.
How to cite: Rybka, H., Leps, N., Mannig, B., Paxian, A., Dalelane, C., Winderlich, K., Steger, C., and Früh, B.: Benchmarking Across Scales: Evaluating EURO-CORDEX CMIP6 Regional Climate Models over Germany, EMS Annual Meeting 2026, Utrecht, Netherlands, 6–11 Sep 2026, EMS2026-476, https://doi.org/10.5194/ems2026-476, 2026.
Sunshine hours are a widely used climate variable with relevance for ecosystems, agriculture, solar energy, and public health that are well understood by the average citizen. However, this variable is not routinely provided as an output variable by regional climate models (RCMs), including those in the CORDEX framework.
Here we develop and calibrate a practical two-stage method to estimate daily sunshine hours from daily surface downwelling shortwave radiation (rsds). The approach is calibrated using Danish observational data with month-specific empirical relationships based on the normalised radiation ratio x = R / Rₐ, where R is daily global radiation at the surface and Rₐ is daily extraterrestrial radiation from FAO-56. We describe how an empirical relationship has been derived from this approach using model selection techniques across a range of possible configurations. Model performance statistics are generally favourable and show a good fit to the observations.
We apply the relationship to outputs from the CORDEX5 and CORDEX6 family of regional climate models to generate projections of sunshine hours with daily resolution. The variable is then used to generate a new set of indicators for inclusion in Klimaatlas, the Danish Climate Atlas, including seasonally resolved mean number of sunshine hours: we find a reduction in winter sunshine hours of up to 13% projected for 2071–2100 under the RCP8.5 emission scenario. Other novel indicators, such as the frequency of “sunshine droughts” – prolonged periods with no or few sunshine hours – have also been developed. Finally, we perform a comparison of the results obtained between CORDEX5 and CORDEX6 models, with a particular focus on understanding differences between these generations of models, and how they relate to the aerosol scenarios used in each model.
How to cite: Thejll, P., Boberg, F., Christensen, O. B., Arnbjerg-Nielsen, K., Düsterhus, A., and Payne, M. R.: Translating Climate Variables into Everday Language: Sunshine Hour Projections from CORDEX Regional Climate Models, EMS Annual Meeting 2026, Utrecht, Netherlands, 6–11 Sep 2026, EMS2026-608, https://doi.org/10.5194/ems2026-608, 2026.
The demand for environmental research-to-applications-to-services efforts to provide “actionable” information is rising, yet the refinement of raw model projection outputs to reliable, local-scale multivariate indices remains fraught with methodological choices that can markedly influence and sometimes distort the final product. We examine the critical role of bias correction (BC) workflow configurations, drawing in part on findings from a study published in the Journal of Applied Meteorology and Climatology (Xu et al., 2026; doi:10.1175/JAMC-D-25-0128.1).
Focusing on the summertime daily maximum Heat Index (HI, a non-linear function of temperature and relative humidity), we evaluate four BC workflows across the Northeast United States to provide findings applicable to many mid-latitude regions. Using thirteen high-quality weather stations for training and validation, we compare one Multivariate Bias Correction (MBCn) approach and three univariate Quantile Delta Mapping (QDM) workflows. A central finding is the extreme sensitivity of a multivariate index such as the HI to the “univariate component-wise” approach, in which temperature and humidity are corrected independently before HI is computed from the bias-corrected variables. While this somewhat common practice yields high-quality probability density functions of the individual temperature and humidity variables, it does not use information from the observational training data to adjust the dynamical model’s inter-variable dependence structure, leading to errors in the frequency of extreme HI days as high as 147%. In contrast, workflows that either apply univariate bias correction directly to the Heat Index calculated from the dynamical model or utilize the MBCn multivariate algorithm to jointly adjust the component variables based on the training data’s inter-variable dependence structure prove far more robust.
The presentation concludes with an analysis of which shortcomings in the dynamical models’ simulation of synoptic weather patterns lead to the largest errors when the univariate component-wise workflow is followed. This kind of analysis could inform researchers who opt to eschew a model democracy approach (i.e., the equal weighting of all models regardless of performance) for a “fit-for-purpose” selection strategy, where dynamical models are selected or rejected based on their ability to maintain the physical dependencies required for reliable multivariate applications.
How to cite: Dixon, K., Xu, W., Lanzante, J., Sun, L., and Zenes, N.: Post-processing Pipeline Uncertainties: Navigating Bias Correction Configurations for Actionable Multivariate Climate Indices, EMS Annual Meeting 2026, Utrecht, Netherlands, 6–11 Sep 2026, EMS2026-55, https://doi.org/10.5194/ems2026-55, 2026.
ClimRisk.eu is a web-map tool for climate change assessment in Europe. ClimRisk allows users to explore climate projections interactively and download data for any specific location across Europe. Originally launched in 2004 as the platform for climate proofing in the Czech Republic and Central Europe, it is now aiming to cover the continental Europe. ClimRisk connects observational data with climate model projections and delivers information on the level of NUTS III region or even finer spatial scale of several hundreds of km2. The underlying data inputs offer even more detailed horizontal resolution ranging from 0,5 km (Czech Republic) to ca 5 km (Europe).
ClimRisk is built on the combination of Copernicus/ECMWF CERRA reanalysis and CMIP6 global climate models. From the wide ensemble of 30 CMIP6 GCMs only 7 models were chosen for the ClimRisk based on the model validation and considering the range of the climate change signal for major climate variables from the entire CMIP6 ensemble. Four Shared Socioeconomic Pathways (SSP) scenarios are considered, and the climate projections are presented either for individual scenarios or their combination. In addition to CMIP6 GCMs, the new convection-permitting regional climate model ALADIN-Climate/CZ is included to improve representation of local processes and extreme precipitation.
For users, the platform provides long-term climatological means of key meteorological variables, including air temperature, precipitation, wind speed, humidity, and solar radiation, together with a wide range of derived climate indices, including those related to extreme events. Additional variables such as soil moisture (derived using the SoilClim model) are also available, making the platform suitable for applications in forestry, agriculture, and hydrological impact studies. The platform also provides information on the uncertainty associated with future climate projections for any selected location.
Acknowledgements.
We acknowledge support from AdAgriF – Advanced methods of greenhouse gases emission reduction and sequestration in agriculture and forest landscape for climate change mitigation (CZ.02.01.01/00/22_008/0004635) and the PERUN project (SS02030040) co-funded by the Technology Agency of the Czech Republic and the Ministry of the Environment under the Programme Environment for Life (Program Prostředí pro život).
How to cite: Skalák, P., Štěpánek, P., Balek, J., Zahradníček, P., Penčevová, R., Meitner, J., Farda, A., Fischer, M., and Trnka, M.: ClimRisk.eu: Regional climate projections for Europe , EMS Annual Meeting 2026, Utrecht, Netherlands, 6–11 Sep 2026, EMS2026-518, https://doi.org/10.5194/ems2026-518, 2026.
In Norway, municipalities hold a significant responsibility for adapting to climate change. As local planning authorities, service providers, owners of critical infrastructure, and community developers they exercise significant local autonomy. Yet many of the 357 Norwegian municipalities (especially the small and medium sized) lack capacity to find, interpret, and apply climate information. The Norwegian Centre for Climate Services (NCCS) provides national fine-scale climate projections, as the knowledge base for climate change adaptation in Norway. County-wise factsheets have been in use for almost a decade, describing future changes in climate, hydrology, and effects on natural hazards at the county level. With the publication of new climate projections in 2025, NCCS set out to deliver municipality-wise climate fact sheets to support local decision makers, by aligning climate data to the expressed needs of local decision-makers.
To achieve this, a co-production framework was developed in collaboration with three pilot municipalities, Rakkestad, Skjåk, and Vestvågøy, selected for their diverse geographies, hazards, and experiences (e.g., drought and agriculture in Rakkestad; floods/avalanches and glacier tourism in Skjåk; storm surge and sea level rise in Vestvågøy). Through written surveys, iterative user testing, digital and in-person workshops, user stories were refined to align with municipal roles and responsibilities. The goal was to design a prototype profile format and content structure that links climate indices and information to users' workflows, decision-making processes, and local risk management.
Central to this co-production process are challenges inherent in balancing simplicity and scientific integrity. For example, how can climate information be filtered to ensure relevance for specific municipalities? How should uncertainty in the data be communicated? When is it necessary to use general statements instead of precise figures? The balance between clarity and credibility is essential.
This talk shares the co-production process, lessons learned, and a replicable model for turning climate data into actionable, mandate-aligned information that strengthens local climate adaptation - particularly in municipalities with limited resources.
How to cite: Garvin, K., Øie Nilsen, J. E., Flått Stangeby, J. I., Brox Nilsen, I., Tunheim, K., and Verpe Dyrrdal, A.: Bridging user needs and climate data for local adaptation in Norway, EMS Annual Meeting 2026, Utrecht, Netherlands, 6–11 Sep 2026, EMS2026-600, https://doi.org/10.5194/ems2026-600, 2026.
Design outdoor temperature (DOT) is a critical parameter for building design, indoor climate regulation, and energy calculations. Historically, these values in Norway were derived from station-based observations with varying methodologies for summer and winter. This presentation outlines a transition to a modernized, unified framework for calculating DOT, as detailed in recent MET Norway reports.
In the first part we introduce a new probabilistic approach for current climate conditions (1991–2020). By utilizing high-resolution (1*1 km) operational gridded climatology, the method overcomes challenges related to sparse station networks and short observation series. The framework employs a Bayesian Generalized Extreme Value (GEV-Bay) distribution to calculate design temperatures for durations of 1 to 5 days and return periods ranging from 2 to 200 years. This approach provides a full distribution of risk rather than a single value, allowing for more robust decision-making in building construction.
In the second part we address the impact of climate change on these design values. Using national climate projections (Klima i Norge 2025), the study compares current DOT values with future estimates for two periods: mid-century (2041–2070) and end-of-century (2071–2100). Analyses for selected locations representing diverse Norwegian climates, indicate that future design summer temperatures are highly sensitive to (global) model selection and emission scenarios.
To facilitate practical climate adaptation, particularly for BREEAM (Building Research Establishment Environmental Assessment Method) certification, a simplified method is proposed. Preliminary results suggest that future global warming impacts can be approximated by adopting a higher return period from the current climate statistics. This provides engineers and architects with a pragmatic tool to account for future heat stress while awaiting more comprehensive national calculations.
How to cite: Hygen, H. O., Grinde, L., Tajet, H. T. T., and Dobler, A.: A Probabilistic Framework for Present and Future Design Outdoor Temperatures in Norway, EMS Annual Meeting 2026, Utrecht, Netherlands, 6–11 Sep 2026, EMS2026-282, https://doi.org/10.5194/ems2026-282, 2026.
Temperatures are increasing, and cold days and prolonged cold periods are becoming less frequent, even in high latitude regions such as Norway. Even though certain years a cold regime occurs and the temperature is low for a period. The Norwegian Meteorological Institute (MET Norway) is currently exploring methods to monitor cold waves, and possibly implement an operational warning system for cold waves or low temperature events in the future.
Cold periods for Norway were analysed for the climatological periods 1961-1990 and 1991-2020. For spatial analyses, an observation-based dataset on a 1 km grid was used. In addition, observations from selected weather stations were studied to capture local effects. Cold periods were identified using different temperature limits of minimum temperature.
A questionnaire was distributed to municipalities across Norway to assess the consequences of cold waves. The survey investigated which measures are implemented during cold periods and the temperature thresholds that trigger action. The responses indicate that cold weather alone is rarely a major issue. However, in combination with other events it becomes a significant contributing factor. The most frequently reported challenges were related to water supply and sewage systems, as well as power outages and heating. Measures and temperature thresholds vary considerably, including support for vulnerable groups and the provision of emergency water supplies. Some municipalities restrict children’s outdoor activities at −10 °C, while others maintain normal operations even during severe cold. Both the duration of cold periods and rapid temperature fluctuations are highlighted as key challenges.
Internationally, a range of thresholds are used to define cold waves and trigger low temperature warnings. MET Norway aims to develop a method suitable for Norwegian conditions, potentially in collaboration with neighbouring countries. Climate Services has worked in cooperation with the forecasting division at MET Norway to propose a unified methodology applicable to both forecasting and climatological analyses, ensuring consistency across services. After finding a suitable definition for cold waves or warning of low temperatures, it will be tested operationally and evaluated.
How to cite: Tajet, H. T. T., Gangstø, R., Hallerstig, M., Agersten, S., Hygen, H. O., Smiths, J., Fagerli, G. O., and Ordahl, J.: Climatological Cold Waves in Norway - a base for Operational Warning System, EMS Annual Meeting 2026, Utrecht, Netherlands, 6–11 Sep 2026, EMS2026-307, https://doi.org/10.5194/ems2026-307, 2026.
How to cite: Saldivia Gonzatti, I., Paparrizos, S., and Biemans, H.: From seasonal climate forecasts to a crop yield early warning system for rainfed agriculture, EMS Annual Meeting 2026, Utrecht, Netherlands, 6–11 Sep 2026, EMS2026-498, https://doi.org/10.5194/ems2026-498, 2026.
The aviation sector has an important socio-economic role both through the transport of goods and services as well as tourism. Recent estimates of the total gross economic impact of the aviation sector are estimated to be approximately €500 billion (2.8% of the total economy) in the European Economic Area, which is expected to grow as demands for global connectivity are ever increasing. With nearly 7 million take-offs and landings of commercial flights recorded at airports in the European Union during 2024, there is also a growing pressure on airports to increase the number of airplane movements.
The frequency of take-off and landings is strongly influenced by the surface and lower-atmospheric conditions near the airports (e.g. temperature, (gust) wind speed, wind direction, precipitation). In unfavourable conditions, this can lead to flights’ diversions and cancellations or delays to assure safe operating conditions, consequently resulting in potential high economic losses for the aviation industry. Knowing these atmospheric conditions is therefore essential for planning routes and airports’ operations.
In this study we investigate the average seasonal surface conditions for a wide range of Mediterranean airports by combining the information from surface observations and the results from high-resolution convection-permitting regional climate models. Using the future simulations of the same models, we also estimate how several key surface variables might differ in a future changing climate. Due to the high resolution of these models (~3 km at hourly resolution) and the length of the simulations, these are among the first simulations that allow us a detailed look at the future atmospheric conditions near individual airports. Providing this essential information will allow a more efficient planning of future airport activities.
Acknowledgements
This research has been carried out with funding from Ministero dell'Università e della Ricerca under the call Fondo Italiano per la Scienza 2022-2023 (FIS-2) for the project "Mediterranean Extreme Events and Tipping elements in a changing climate on multiple spatiotemporal scales", grant number FIS-2023-00159, CUP: D53C24005450001.
How to cite: de Leeuw, J., Zazulie, N., Siciliano, F., Scardino, G., Coppola, E., Faranda, D., and Alberti, T.: A Study of Meteorological Conditions at Mediterranean Airports in a Changing Climate using Convection-Permitting Regional Climate Models, EMS Annual Meeting 2026, Utrecht, Netherlands, 6–11 Sep 2026, EMS2026-580, https://doi.org/10.5194/ems2026-580, 2026.
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