Climate change is increasingly affecting natural, societal and economic systems in Switzerland, requiring a better understanding of cross-sectoral risks and their interactions. This challenge is addressed by the programme “NCCS-Impacts” of the Swiss National Centre for Climate Services (NCCS), in which five interlinked projects cover (1) socioeconomic scenarios, (2) human and animal health, (3) ecosystem services, (4) supply chains, and (5) economic costs. The projects are closely connected and generate strong synergies. Results will be released progressively until the end of 2026.In addition to generating new scientific insights, NCCS-Impacts places strong emphasis on developing actionable, user-oriented climate services. These are co-produced by researchers, practitioners, stakeholders and communication experts to maximise their usability and relevance for climate adaptation and mitigation. At the programme level, a key objective is to synthesise results across sectors and disciplines in a consistent and structured way making complex and heterogeneous findins more accessible, comparable and relevant for decision-making.The presentation provides a synthesis of key results from the projects, including new socioeconomic pathways for Switzerland and associated greenhouse gas emissions, projections of heat-related mortality and vulnerability risks, climate risks for supply chains, and impacts on agricultural yields. It will also showcase selected web-based tools tailored to user needs, such as a hospital management tool for forecasting heat-related emergency visits, an interactive map for identifying supply chain risks, and a dashboard for exploring cross-sectoral impacts on ecosystem services. In addition, the presentation will introduce the overarching synthesis concept developed within NCCS-Impacts, illustrating how cross-sectoral findings can be integrated, structured and communicated to support informed decision-making.

This study presents a detailed analysis of the CORDEX-FPS multi-model ensemble of convection-permitting climate simulations over the greater Alpine region. These simulations cover 10-year time slices and were obtained by downscaling global climate model (GCM) projections, using regional climate models (RCMs) and kilometer-scale convection-permitting models (CPMs). Our analysis over the Alpine area agrees with previous studies in terms of projected summer precipitation changes for the end of the century, in particular regarding a decrease in mean precipitation and increases in hourly precipitation intensities. In addition, we assess projected changes over different subregions, provide analyses at monthly and seasonal basis for temporal aggregations ranging from 1 hr to 5 days, address different extreme precipitation indices, and present validation against an Alpine-scale daily precipitation data set and an hourly precipitation product based on 3 Doppler radars. The evaluation reveals that CPMs show a refinement of spatial patterns, reduce the overestimation of precipitation frequency, and better capture intense precipitation characteristics. The improvements are especially apparent on the sub-daily scale and in the summer season. Convection-Permitting Model climate projections show an increase in precipitation intensity for all seasons and across all temporal aggregations in all regions, except for the Mediterranean in summer. The projections from different CPMs qualitatively agree, despite significant differences in the GCMs circulation changes, suggesting that the increase in heavy events is primarily due to thermodynamic effects. We also present a hypothesis explaining why projections of relative changes in hourly precipitation percentiles are similar between CPMs and RCMs, despite large biases in RCMs.

Impact modelling requires fine-scale climate information to simulate possible impacts of climate change on different sectors such as agriculture, water management or food production. Such impact models are run at a much finer spatial and temporal resolution than global or regional climate models, and therefore a pre-selection of climate model chains is required due to the computational limitations of these models. To date, there is no structured guidance for practitioners and impact modelers on how to select climate model chains. This is also the case for the Swiss Climate Scenarios (CH2018), which main products are usually communicated to the users as median, upper and lower estimates calculated for each product and time slice individually. In this work, we present a new sub-selection climate ensemble method tailored to the users’ needs and the desired emission scenario (Representative Concentration Pathways, RCP). The method builds on the core statements of the CH2018, i.e., droughts, heat waves, heavy rainfalls, and snow-scarce winters, and complements them with three further application cases, i.e., temperature, precipitation, combined temperature and precipitation. For each application case and each RCP, three representative climate model chains are selected from the full ensemble to cover the range of the climate change signal. These include one chain corresponding to the upper, middle and lower limits of the ensemble range. The selection of climate model chains is based on the climate change signals calculated for a set of pre-selected climate indicators (e.g., mean temperature or number of hot days). Next, each climate model chain is ranked for each climate indicator according to its climate change signal calculated between the end of the century and the CH2018 reference period (i.e., 2070-2099 vs. 1981-2010). This ranking is used to divide the models into three terciles, representing the upper, lower and middle bounds of the ensemble. For each tercile, one climate model chain is next selected that best meets the selection criteria. As a result, a sub-selected ensemble with three climate model chains is proposed to the users. The method has been developed for Switzerland and five major Swiss regions using the CH2018 GRIDDED dataset, which contains of 68 daily, transient and bias-corrected simulations of climate model chains covering the simulation period of 1981-2099. The method allows the CH2018 users to choose from three RCPs (RCP2.6, RCP4.5 and RCP8.5) and seven application cases to obtain a set of three representative climate model chains. The selected climate model chains were next successfully implemented in a hydrological impact model to assess their applicability for assessing climate impacts on hydrological variables. The method is very flexible and can easily be applied to a new or an extended climate model ensemble or to newly defined application cases.

Short-duration extreme rainfall events are the main trigger for natural hazards such as flash floods and debris flows. As a result of climate change, extreme convective summer events are expected to intensify markedly in mountainous regions such as the Swiss Alps, although the magnitude of intensification on a sub-daily basis is uncertain. Here, we quantify potential future changes in Swiss sub-daily extreme rainfall using the physically-based TENAX model, which captures the dependence of extreme rainfall on temperature. An independent evaluation against MeteoSwiss observation-based extreme value analyses shows that TENAX estimates differ by less than 10% at 60% of stations for 10 min and 72% for hourly durations over the 10-year return period. TENAX-estimated rainfall-temperature scaling rates in Switzerland are around 10% °C −1 for 10-min extremes and 7% °C −1 for hourly durations. Applied with the Klima CH2025 climate projections that are CMIP5-based, results indicate that by the time global warming attains a 3 °C temperature increase compared to present-day conditions, 10-min rainfall return levels could increase by up to 40%, while hourly extremes intensify by approximately 20%. The projected changes exhibit strong spatial variability, with high-altitude regions experiencing greater intensification than lowlands due to stronger rainfall-temperature scaling and an amplified warming rate. Despite an overall reduction in summer rainfall event frequency, extreme events are expected to occur more frequently, with current 100-year return levels projected to shift to 30-year return periods in some regions. These results highlight the growing flood risk in Swiss cities and the increasing threat of rainfall-driven hazards in mountainous areas, underscoring the urgent need for proactive adaptation measures.

Climate model ensembles serve as an input to all impact studies that use sector-specific models (e.g., hydrological, ecological, crop models, fire hazard) at regional or local scales. These models require regionally scaled cli- mate information to simulate the potential environmental and socio-economic sectoral impacts of climate change. Such simulations are based on comprehensive multi-member climate ensembles derived from the bias-adjusted and downscaled global and regional climate models. Due to limited computational resources, users of climate scenarios often can only include a small number of the ensemble members in their calculations, and therefore they often select them at random. A pre-selection of meaningful, consistent and case-specific members is therefore desired by the climate data users. In this work, we aim to fill this gap and present a novel user-tailored procedure for sub-selecting ensemble members for a variety of applications. Our method is based on the ranking of the climate change signal (CCS) calculated for a set of climate indices (e.g., mean temperature or number of hot days). Based on the CCS strength, three ensemble members representing the strongest, weakest, and median CCS are selected for each application. We also demonstrate the robustness of our approach in a specific hydrological impact model framework. Providing a systematic procedure not only assists impact modelers in selecting appropriate members, but also improves the consistency and comparability of different impact studies.

Konsistente und breit anwendbare nationale Klimaszenarien sind eine zentrale Grundlage für die Abschätzung von zukünftigen Klimarisiken. In der Schweiz werden solche Referenzszenarien im Auftrag des Bundes regelmässig von Meteo-Schweiz, ETH Zürich und weiteren Partnern erarbeitet und auf aktuellstem Stand gehalten. Die derzeit gültigen Klimaszenarien CH2018 für die Schweiz stellen eine Reihe von nutzerfreundlichen Produkten und Datensätzen unter anderem für die Risikoanalyse zur Verfügung. In diesem Beitrag geben wir eine kurze Übersicht der CH2018-Szenarien, ihrer zugrundeliegenden Modelldatensätze und Methoden sowie ihrer möglichen Limitierungen in der Anwendung. MeteoSchweiz unterstützt Nutzende gerne bei der Verwendung und Interpretation von CH2018. Im derzeit laufenden Projekt «Klima CH2025» werden die aktuellen Klimaszenarien CH2018 überarbeitet und aufdatiert. Die neuen Datensätze und Produkte werden Nutzenden ab November 2025 frei zur Verfügung stehen.

With ongoing climate change, national climate scenarios based on current scientific knowledge are indispensable for developing regional and local mitigation and adaptation strategies. In this context, the project Klima CH2025 currently develops the upcoming edition of Swiss climate change scenarios, facilitated by collaborative efforts involving the Swiss Federal Office of Meteorology and Climatology MeteoSwiss, ETH Zurich and further partners. Klima CH2025 will provide an updated basis of user-relevant climate information and related products containing regional and local assessments of future climate change in Switzerland. The backbone of the data production chain within Klima CH2025 is a comprehensive ensemble of CMIP-driven EURO-CORDEX climate simulations. These simulations are bias-adjusted and downscaled through quantile mapping, ensuring the reliability and accuracy of the derived climate scenarios on a localized level. By combining model simulations and observations through quantile mapping, also regional to local scale heat indicators representing the current and future climates are computed. These heat indicators portraying both current climatic conditions and projected future trends are presented in a global warming level framework. The significance of understanding heat-related extremes within a changing climate cannot be overstated. Such extremes exert direct and indirect impacts on several levels such as the human well-being and critical infrastructure. Thus, gaining insights into changes of heat extremes is crucial for formulating effective adaptation strategies and provides an important basis for decision-making across different sectors. In this contribution, our aim is to validate the representation of different heat extreme indicators formulated within the framework of Klima CH2025 and present their projected future changes for different global warming levels.

National climate scenarios reflecting the current scientific state of knowledge are an indispensable basis for public and private sectors to plan and design adaptation and mitigation measures. Regional or even local assessments of future climate change are therefore an important climate service. The next generation of climate scenarios for Switzerland is currently under development in the project Klima CH2025. Similar to previous climate scenario generations, the new project is a joint effort involving the Federal Office of Meteorology and Climatology MeteoSwiss, ETH Zurich, C2SM and further partners from academia and administration. The main goal of Klima CH2025 is to develop, update and provide the physical basis of climate change in Switzerland and related products. Two main scientific questions will be addressed: 1) How can we better merge observations and model-based climate scenarios in order to provide consistent and temporally seamless information to best serve user needs?; 2) What is the projected evolution of impact-relevant climate extremes in Switzerland and what are their underlying processes? Guided by these two questions, we will develop a range of new products, engage with stakeholders, and plan active communication and dissemination.We build our scenarios upon the existing CMIP5-based EURO-CORDEX simulations but combine them with CMIP6 GCM information using a variant of a pattern scaling approach. Our approach bridges the gap between different CMIP and CORDEX generations and at the same time merges models and observations to provide consistent information on climate change for the past, the present and the future. In this presentation, the general approach of the Klima CH2025 framework will be presented together with the method applied to bridge models and observations and to integrate CMIP6 evidence into CMIP5-based EURO-CORDEX simulations. In addition, first results of the project and planned products will be presented.

This study presents the detailed analysis of a novel and first-of-its-kind 10-year multi-model ensemble of kilometer-scale convection-permitting climate model (CPM) simulations over the Greater Alpine Region. The simulations were obtained by downscaling global climate model (GCM) projections using regional climate models (RCMs) and further downscaling to the kilometer scale using convection-permitting climate models (CPMs) . This study evaluates the CPMs and assesses their added value with respect to RCMs regarding basic and heavy precipitation characteristics. In addition, this study assesses projected changes for the end of the century. The analysis is performed for climatological seasons, for different temporal aggregations between 1 hour and 5 days, and for various precipitation indices.. ERA-Interim-driven and historical GCM-driven CPM simulations are compared against daily and hourly observational datasets, as well as their driving RCM counterparts to evaluate their performance and added value. Evaluation reveals that CPMs refine spatial patterns, reduce the overestimation of precipitation frequency and better capture intense precipitation characteristics, especially on the sub-daily scale and in summer. Climate change projections show an intensification of precipitation for all seasons and across all temporal aggregation levels. During summer, mean precipitation and precipitation frequency are projected to decrease, especially in the Mediterranean. In winter, an increase is projected across most parts of the Alps. CPMs and RCMs show agreement, with CPMs indicating slightly amplified signals and reduced model spread. The findings are consistent with previous studies using individual simulations, but provide one of the first multi-model assessment of projections in heavy precipitation over the Alps.

Consistent and up-to-date national climate scenarios are an indispensable basis for public and private sectors to plan and design adaptation and mitigation measures. Regional or even local assessments of future climate change are therefore an important climate service. The latest edition of Swiss Climate Scenarios CH2018 was released in 2018 (www.climate-scenarios.ch). Overall, a further increase of mean temperatures is projected, accompanied by four highly impact-relevant facets: more intense and more frequent precipitation extremes, more intense and more frequent hot extremes, drier summers, and snow-scarce winters. Since the release of the CH2018 scenarios, science as well as user needs have evolved. Scientific advances such as those documented in the latest IPCC report (AR6) have been published and new high-resolution convection-permitting climate models have been developed. Thanks to our ongoing user engagement and consultancy, a more detailed landscape of user requirements was established. In the recently launched project Klima CH2025, identified gaps in the existing climate scenarios and additional user requirements will now be addressed. Similar to previous climate scenario generations, the new project is a joint effort involving the Federal Office of Meteorology and Climatology MeteoSwiss, ETH Zurich and further partners from academia and administration. The results of Klima CH2025 will be based on the existing CH2018 scenarios and will extend them with new scientific insights and products. Two main scientific questions will be addressed: 1) How can we better merge observations and model-based climate scenarios in order to provide consistent and temporally seamless information to serve user needs?; 2) What is the projected evolution of impact-relevant climate extremes in Switzerland and what are their underlying processes? Guided by these two questions, we will develop a range of new products, engage with stakeholders, plan active communication and dissemination. In this presentation, the general approach of Klima CH2025 as well as first results will be presented.

MeteoSwiss is currently implementing a new NWP postprocessing suite for providing automated local weather forecasts to the general public. As these forecasts are nowadays mainly accessed via smartphone app, we aimed at global postprocessing approaches, that is, optimizing forecasts not only at observation sites but at any location in Switzerland. The system takes advantage of both regional area and global NWP ensemble models with different forecast horizons for providing seamless probabilistic predictions over two weeks leadtime. Finally, the postprocessing suite also considers operational aspects such as robustness towards missing or delayed input data or the ability to cope with limited reforecasts records for training.Both ensemble model output statistics (EMOS) and machine learning (ML) methods are applied for postprocessing the target parameters temperature, precipitation, wind and cloud cover. Forecast skill in terms of CRPS improves by up to 30% compared to direct model output, with largest benefits for temperature and wind in areas of complex orography and only marginal gains for precipitation during seasons with a high fraction of convective situations. The postprocessing of multiple NWP sources not only allows seamless forecasts, but also proved more skillful than single-model postprocessing. EMOS postprocessing performed well even in case only short reforecast records were available, but was outperformed by ML approaches given sufficient training data. While this general-purpose postprocessing suite improves forecasts overall, it showed weaknesses in some warning-relevant weather situations. Future developments will aim at extending its applicability to these less frequent situations, target further parameters, and extending the use of ML methods.

A comprehensive assessment of twenty-first century climate change in the European Alps is presented. The analysis is based on the EURO-CORDEX regional climate model ensemble available at two grid spacings (12.5 and 50 km) and for three different greenhouse gas emission scenarios (RCPs 2.6, 4.5 and 8.5). The core simulation ensemble has been subject to a dedicated evaluation exercise carried out in the frame of the CH2018 Climate Scenarios for Switzerland. Results reveal that the entire Alpine region will face a warmer climate in the course of the twenty-first century for all emission scenarios considered. Strongest warming is projected for the summer season, for regions south of the main Alpine ridge and for the high-end RCP 8.5 scenario. Depending on the season, medium to high elevations might experience an amplified warming. Model uncertainty can be considerable, but the major warming patterns are consistent across the ensemble. For precipitation, a seasonal shift of precipitation amounts from summer to winter over most parts of the domain is projected. However, model uncertainty is high and individual simulations can show change signals of opposite sign. Daily precipitation intensity is projected to increase in all seasons and all sub-domains, while the wet-day frequency will decrease in the summer season. The projected temperature change in summer is negatively correlated with the precipitation change, i.e. simulations and/or regions with a strong seasonal mean warming typically show a stronger precipitation decrease. By contrast, a positive correlation between temperature change and precipitation change is found for winter. Among other indicators, snow cover will be strongly affected by the projected climatic changes and will be subject to a widespread decrease except for very high elevation settings. In general and for all indicators, the magnitude of the change signals increases with the assumed greenhouse gas forcing, i.e., is smallest for RCP 2.6 and largest for RCP 8.5 with RCP 4.5 being located in between. These results largely agree with previous works based on older generations of RCM ensembles but, due to the comparatively large ensemble size and the high spatial resolution, allow for a more decent assessment of inherent projection uncertainties and of spatial details of future Alpine climate change.

To make sound decisions in the face of climate change, government agencies, policymakers and private stakeholders require suitable climate information on local to regional scales. In Switzerland, the development of climate change scenarios is strongly linked to the climate adaptation strategy of the Confederation. The current climate scenarios for Switzerland CH2018 - released in form of six user-oriented products - were the result of an intensive collaboration between academia and administration under the umbrella of the National Centre for Climate Services (NCCS), accounting for user needs and stakeholder dialogues from the beginning. A rigorous scientific concept ensured consistency throughout the various analysis steps of the EURO-CORDEX projections and a common procedure on how to extract robust results and deal with associated uncertainties. The main results show that Switzerland’s climate will face dry summers, heavy precipitation, more hot days and snow-scarce winters. Approximately half of these changes could be alleviated by mid-century through strong global mitigation efforts. A comprehensive communication concept ensured that the results were rolled out and distilled in specific user-oriented communication measures to increase their uptake and to make them actionable. A narrative approach with four fictitious persons was used to communicate the key messages to the general public. Three years after the release, the climate scenarios have proven to be an indispensable information basis for users in climate adaptation and for downstream applications. Potential for extensions and updates has been identified since then and will shape the concept and planning of the next scenario generation in Switzerland.

MeteoSwiss is developing and implementing a post-processing suite of multi-model ensemble forecasts to produce seamless probabilistic calibrated forecasts at arbitrary locations in Switzerland (i.e. also for un-observed locations). With the complex topography of Switzerland, the raw output of the numerical model is subject to particular strong biases and conditional errors. Here, we present results for hourly temperature and precipitation predictions. We apply a global ensemble model output statistics (gEMOS) framework. It extends the classical EMOS approach by incorporating static predictor variables describing relevant topographical features and it is trained for all stations together using a 4-year multi model numerical weather prediction (NWP) archive. As NWP sources, we combine data from the COSMO model suites (1.1 and 2.2 km horizontal grid-spacing) and from the ECMWF IFS medium-range forecasting system. Note that the three NWP suites have different forecast horizons. We show that gEMOS is able to improve forecasts for both variables. Depending on selection of predictors, lead-time, hour-of-day and season we find improvements up to 30% in terms of CRPS for both variables with most pronounced improvements in mountainous regions. Particularly for temperature, the multi-model combination further increases the forecast skill compared to postprocessing using high-resolution simulations of COSMO only. While locally optimized approaches show better performance in terms of skill at the observing sites, the advantage of gEMOS lies in the ability to generate calibrated predictions for arbitrary locations in a consistent way. Its computational efficiency makes it a particularly attractive method for operationalization in a realtime context.

MeteoSwiss has developed and is currently implementing a NWP postprocessing suite for providing automated weather forecasts at any location in Switzerland. The aim is a combined postprocessing of high resolution limited area and global model ensembles with different forecast horizons to enable seamless probabilistic forecasts over two weeks leadtime. Further, the output should be coherent in space and provide predictions at any location of interest, including sites without observations. We use the full archive of MeteoSwiss’ operational local area models (COSMO-1 and COSMO-E) over the past four years and the corresponding IFS-ENS medium range predictions of ECMWF to develop postprocessing routines for temperature, precipitation, cloud cover and wind. Here we present selected key results on the performance of various postprocessing methods we applied but also on practical aspects of their implementation into operational production. Both ensemble model output statistics (EMOS) and machine learning (ML) approaches are able to improve the forecasts in terms of CRPS by up to 30% as compared to the direct output of the local area model. The skill increase obtained by postprocessing varies depending on the parameter, region and season, with best results for temperature and wind in areas of complex orography and only marginal improvements for precipitation during seasons with a high fraction of convective situations. Particularly for temperature, the combined postprocessing of COSMO and IFS-ENS resulted in a skill benefit over postprocessing the COSMO models alone. Locally optimized postprocessing would allow further skill improvements, but only at sites where observations are available. However, the ability of non-local postprocessing approaches to provide calibrated forecast at any point in space is a key advantage for providing automated forecasts to the general public via the internet and smartphone app. Furthermore, the computational efficiency of these non-local approaches makes them attractive for operationalization in a realtime context.

Statistical postprocessing is routinely applied to correct systematic errors of numerical weather prediction models (NWP) and to automatically produce calibrated local forecasts for end-users. Postprocessing is particularly relevant in complex terrain, where even state-of-the-art high-resolution NWP systems cannot resolve many of the small-scale processes shaping local weather conditions. In addition, statistical postprocessing can also be used to combine forecasts from multiple NWP systems. Here we assess an ensemble model output statistics (EMOS) approach to produce seamless temperature forecasts based on a combination of short-term ensemble forecasts from a convection-permitting limited-area ensemble and a medium-range global ensemble forecasting model. We quantify the benefit of this approach compared to only processing the high-resolution NWP. We calibrate and combine 2-m air temperature predictions for a large set of Swiss weather stations at the hourly time-scale. The multi-model EMOS approach ('Mixed EMOS') is able to improve forecasts by 30\% with respect to direct model output from the high-resolution NWP. A detailed evaluation of Mixed EMOS reveals that it outperforms either single-model EMOS version by 8-12\%. Valley location profit particularly from the model combination. All forecast variants perform worst in winter (DJF), however calibration and model combination improves forecast quality substantially.

Eine zentrale Aufgabe und Herausforderung heutiger Klimaservices ist die Bereitstellung wissenschaftlich verlässlicher, robuster und gleichzeitig anwendbarer und anwendungsfreundlicher Simulationen des zukünftigen Klimas. Meist bauen solche Klimaprojektionen auf globalen und/oder regionalen Klimamodelldaten auf und werden auf nationaler Skala von den jeweiligen (hydro)meteorologischen Einrichtungen und Konsortien bereitgestellt. Sie dienen als wissenschaftliche Grundlage für Klimaanpassungsmaßnahmen und Mitigations-Strategien. Während in den letzten Jahren eine Reihe an nationalen Ensembles von Klimasimulationen erstellt wurde, entwickelt sich die zugrundeliegende Wissenschaft stetig weiter, und die derzeit verfügbaren nationalen Klimaprojektionen unterliegen unterschiedlichen Annahmen und Einschränkungen. Eine dieser Einschränkungen ist die Inkonsistenz über nationale Grenzen hinweg aufgrund von Unterschieden in den verwendeten Modellensembles, Postprocessing-Methoden, Beobachtungsdatensätzen, Klimaindikatoren und Kommunikations- und Darstellungsansätzen. Angesichts dieser Problematik haben die drei nationalen Wetterdienste des D-A-CH-Raumes, DWD (D), ZAMG (A) und MeteoSchweiz (CH) das sog. „D-A-CH-Szenarienprojekt“ ins Leben gerufen. Im Rahmen dieses Projekts sollen jene Arbeitsschritte des Entstehungsprozesses neuer nationaler Klimasimulationen, die vornehmlich Ursachen für Inkonsistenzen in Grenzregionen darstellen, gemeinsam und unter weitgehender Vereinheitlichung bearbeitet werden. Das Ziel der Kooperation ist es die nächste Generation nationaler Szenarien durch Vereinheitlichungen im methodologischen Bereich und durch Wissensaustausch weitestgehend konsistent zu gestalten. Wegen unterschiedlicher nationaler Anforderungen, Zeitpläne und klimatologischer Gegebenheiten ist aber keine vollkommene Vereinheitlichung geplant. Neben einer Verbesserung der Qualität und Konsistenz der nationalen Klimasimulationen in Deutschland, Österreich und der Schweiz, können durch die Zusammenarbeit Synergien genutzt und Ressourcen gespart werden. Das „D-A-CH-Szenarienprojekt“ befindet sich nach seinem Start im Mai 2021 in einer frühen Phase. Derzeit stehen grundlegende Themen wie die zur Verfügung stehenden Beobachtungsdatensätze, die regionalen Klimasimulationen und deren Verbesserung im Vordergrund. Weiter sind gemeinsame Aktivitäten in den Bereichen Modellevaluierung, Ensemble-Auswahl und statistische Regionalisierungs- und Fehlerkorrekturverfahren geplant. Mit weiterem Fortschritt der Kooperation sollen zunehmend konkrete Nutzer eingebunden und deren Bedarf an Szenarienprodukten berücksichtigt werden. Die Präsentation im Rahmen der Tagung soll die gemeinsam entwickelte Strategie und die damit verbundenen Chancen und Herausforderungen vorstellen und eine Erstinformation für potentielle Anwender der Klimaservices und aus der Impakt-Community bieten.

<p>Wir pr&#228;sentieren eine umfassende Analyse des alpinen Klimawandels im 21. Jahrhundert basierend auf den regionalen Klimasimulationen der EURO-CORDEX Initiative. Klima&#228;nderungssignale verschiedener Temperatur- und Niederschlagsindikatoren sowie der nat&#252;rlichen Schneedecke bis zum Ende des Jahrhunderts wurden unter expliziter Ber&#252;cksichtigung der Modellunsicherheiten und f&#252;r drei Treibhausgas-Emissionsszenarien (RCPs 2.6, 4.5 und 8.5) ausgewertet. Die Ergebnisse zeigen eindeutig, dass der Alpenraum im Laufe des Jahrhunderts mit einer weiteren Erw&#228;rmung zu rechnen hat. Deren Intensit&#228;t h&#228;ngt stark vom jeweils betrachteten Emissionsszenario ab und d&#252;rfte w&#228;hrend des Sommers und f&#252;r Regionen s&#252;dlich des Alpenhauptkamms am st&#228;rksten ausfallen. Je nach Jahreszeit gibt es Tendenzen zu einer leicht verst&#228;rkten Erw&#228;rmung in mittleren und hohen Lagen der Alpen. Die Unterschiede in der mittleren Temperatur&#228;nderung zwischen den Modellsimulationen k&#246;nnen betr&#228;chtlich sein, jedoch sind die Hauptmuster der projizierten Erw&#228;rmung konsistent &#252;ber das gesamte Ensemble und robust. Bei den mittleren jahreszeitlichen Niederschlagsmengen zeigt sich eine Tendenz zur Verschiebung vom Sommer in Richtung Winter, wobei hier noch eine relativ grosse Modellunsicherheit besteht und einzelne Simulationen von diesem generellen Muster abweichen k&#246;nnen. Hohe t&#228;gliche Niederschlagsmengen werden in alles Jahreszeiten tendenziell zunehmen w&#228;hrend die Niederschlagsh&#228;ufigkeit in den Sommermonaten abnimmt. Es zeigt sich ein zum Teil deutlicher Zusammenhang zwischen der projizierten &#196;nderung jahreszeitlicher Mitteltemperaturen und der mittleren Niederschlagsmengen, der von der jeweils betrachteten Jahreszeit abh&#228;ngt: Simulationen und Regionen mit starker Erw&#228;rmung weisen im Sommer eine recht deutliche Niederschlagsabnahme, im Winter aber eine vergleichsweise hohe Niederschlagszunahme auf. Die nat&#252;rliche Schneedecke des Alpenraums wird von den erwarteten &#196;nderungen bei Temperatur und Niederschlag deutlich beeinflusst und wird sich zumindest in tiefen und mittleren H&#246;henlagen merklich zur&#252;ckziehen. Unsere Ergebnisse best&#228;tigen generell die Erkenntnisse aus vorherigen Studien. Sie erlauben durch das grosse ausgewertete Modellensemble und die relativ hohe r&#228;umliche Aufl&#246;sung der Simulationen aber robustere Aussagen zu kleinr&#228;umigen Facetten des zuk&#252;nftigen alpinen Klimawandels und zur Projektionsunsicherheit.</p>

We present an analysis of extreme precipitation events in convection-resolving climate simulations. The simulations are performed with the COSMO-CLM model at 2.2 km resolution across an extended Alpine region and its larger-scale surrounding. Generalized extreme value theory (GEV) is applied to address projections of 5-day, daily and hourly extreme precipitation events in all seasons. Validation using ERA-Interim driven simulations reveals significant improvements with the 2.2 km resolution. In comparison to its driving 12 km model, high resolution improves the simulation of precipitation on most investigated timescales and seasons. The climate change signal is analyzed in 10-year long control and scenario simulations (1991–2000 and 2081–2090) driven by a CMIP5 coupled climate model (MPI-ESM-LR) under an RCP8.5 greenhouse gas scenario. Analysis shows negligible differences between the two resolutions for winter precipitation on all time scales, while in the other seasons the 2.2 km model shows smaller changes in extreme hourly precipitation, and yields narrower uncertainty estimates. Changes in extreme summer precipitation qualitatively scale with the Clausius–Clapeyron rate, i.e., 6–7% per degree warming, and are consistent with previous percentile based analysis. In winter, changes exceed the Clausius–Clapeyron rate. Some interpretations of this result are provided.

The latest Swiss Climate Scenarios (CH2018), released in November 2018, consist of several datasets derived through various methods that provide robust and relevant information on climate change in Switzerland. The scenarios build upon the regional climate model projections for Europe produced through the internationally coordinated downscaling effort EURO-CORDEX. The simulations from EURO-CORDEX consist of simulations at two spatial horizontal resolutions, several global climate models, and three different emission scenarios. Even with this unique dataset of regional climate scenarios, a number of practical challenges regarding a consistent interpretation of the model ensemble arise. Here we present the methodological chain employed in CH2018 in order to generate a multi-model ensemble that is consistent across scenarios and is used as a basis for deriving the CH2018 products. The different steps involve a thorough evaluation of the full EURO-CORDEX model ensemble, the removal of doubtful and potentially erroneous simulations, a time-shift approach to account for an equal number of simulations for each emission scenario, and the multi-model combination of simulations with different spatial resolutions. Each component of this cascade of processing steps is associated with an uncertainty that eventually contributes to the overall scientific uncertainty of the derived scenario products. We present a comparison and an assessment of the uncertainties from these individual effects and relate them to probabilistic projections. It is shown that the CH2018 scenarios are generally supported by the results from other sources. Thus, the CH2018 scenarios currently provide the best available dataset of future climate change estimates in Switzerland.