Abstract. Compound heat and drought events have severe socio-economic impacts on human health, agriculture and electricity supply. While these compound extremes are projected to intensify under climate change, our understanding of their subseasonal predictability remains limited compared to that of individual heat or drought events. In this study, we evaluate the predictability of compound heat and drought events over Europe using the subseasonal prediction system of the European Centre for Medium-Range Weather Forecasts (ECMWF). We find that the physical coupling between heat and drought contributes up to 10 % towards an increase in forecast skill when heat and drought co-occur, relative to a baseline that assumes independence between extremes. However, in regions where the physical coupling between heat and drought via land-surface interaction is misrepresented, compound skill can be lower than when drought are predicted in isolation. These findings highlight the critical role of accurately simulating land-surface feedbacks to improve the reliability of the subseasonal prediction for compound extremes.

The rapid intensification of permafrost warming and thawing in the Swiss Alps due to anthropogenic climate change is well observed and documented, but the response to atmospheric temperature variability on shorter timescales of days to a season is less explored. Here, we address this research question for an ice-poor permafrost slope on the Swiss mountain peak Schilthorn. Using Swiss Permafrost Monitoring Network (PERMOS) observations, we provide evidence that the year-to-year variability of total atmospheric heat over the snow-free summer period largely determines the year-to-year variability of total heat diagnosed in the active layer from the start of the snow-free summer period until the subsequent spring, since the snowpack effectively decouples the ground from the atmosphere during the rest of the year. With the help of idealized sensitivity simulations with the land surface model SNOWPACK, we further demonstrate that late-summer-to-early-autumn heatwaves increase ground heat at the end of the snow-free period until the subsequent spring more than early-summer heatwaves do, even if the total atmospheric heat over the snow-free period remains the same. The reason is that late atmospheric heat occurs closer to the return date of the insulating snowpack and can thus be better retained by the ground than early heat, which is lost back into the atmosphere long before the snowpack comes back. In essence, we provide evidence that hot summers with individual heatwaves in late summer and early autumn might pose the largest risk for a deepening of the active layer and thus for permafrost warming and thawing, at least for ice-poor permafrost investigated here. Running ground surface models such as SNOWPACK with output from subseasonal and seasonal atmospheric prediction models might thus be beneficial for early warnings of ground temperature and permafrost anomalies.

Extratropical cyclones are the main cause of extreme surface weather events in the Mediterranean such as heavy precipitation, floods, severe winds, and dust storms. However, the accuracy in predicting the timing, location, and intensity of such events is often insufficient, which is typically related to errors in cyclone position, propagation, and intensity. In this two-part study we use operational ensemble forecasts from the European Centre for Medium-Range Weather Forecasts to quantify the predictability of extreme surface weather conditions linked to Mediterranean cyclones. We apply an object-based approach to attribute events of extreme precipitation and surface winds to Mediterranean cyclones. Thereby, objects of extreme surface weather are identified at grid points that exceed the seasonal 99th percentile of these parameters and matched to cyclones based on their distance to the cyclone center. In this first part, we introduce the probabilistic method and three illustrative case studies of Mediterranean cyclones that occurred between November 2022 and September 2023, including the infamous Storm Daniel as well as Storms Denise and Jan. We find that the cyclones as well as their attributed objects of extreme surface weather are predicted well for lead times <= 48 h. However, for longer lead times there is large case-to-case variability in the ensemble performance. Predictions of extreme surface weather objects are found to be more uncertain (i) for smaller and less coherent objects, (ii) if the associated cyclone is captured by fewer ensemble members, and (iii) during the earlier stage of the cyclones' lifecycle. The methodological development and its application documented in this paper provide the basis for a multi-year investigation of the predictability of extreme weather linked to Mediterranean cyclones in the second part of this study.

The operational use of subseasonal atmospheric predictions remains a major challenge due to the intermittency of skill on these timescales. Often only specifically trained users can keep track of so-called "windows of forecast opportunity" (WFOs). WFOs are periods during which prediction skill is enhanced because specific states of the atmosphere, ocean, or land surface temporarily enhance predictability. Here, we propose a novel method to combine simultaneously active WFOs into a single opportunity index, which can be used operationally like a traffic-light system and without expert knowledge to anticipate enhanced or reduced subseasonal prediction skill. For predictions of two-weekly and monthly mean temperature anomalies in Switzerland during summer-a region and season with particularly low predictability-we demonstrate that skill can nearly double for a high opportunity index compared with a low one. The use of such an index could thus advance the year-round operational usability of subseasonal predictions in many other regions of the world.

Given the limited skill of precipitation forecasts, the question arises to what extent ensemble forecasting systems can be used for early warning systems that require longer lead times, such as drought early warning. In this study, we use ECMWF’s IFS extended range forecasts, statistically downscaled to a 2 km grid encompassing Switzerland, to quantify the spatially and seasonally stratified predictability of several precipitation statistics. Consistent with existing analyses we find the predictability of extratropical instantaneous precipitation to be limited to week 1. However, when considering accumulated precipitation and the standardized precipitation index (SPI) forecasts, which is commonly used for drought management, the forecasts are skillful well into week 3. This extension in predictability horizon is attributed to the characteristic of accumulated precipitation, which is less sensitive to differences in timing of precipitating systems. The enhanced predictability of SPI enhances the utility of extended range forecasts for monthly drought forecasts. We discuss the practical applicability of these findings in the context of the new Swiss drought early warning and monitoring platform, planned for operations in 2025. Leveraging the enhanced predictability of SPI, this platform stands to benefit from our research outcomes, providing stakeholders with tools for proactive drought management and response strategies.

Alpine permafrost thawing due to climate warming has been rapidly intensifying in the past decades. Since permafrost stabilizes the rock, its thawing has and will become a growing risk for mountainous countries like Switzerland, with potential implications for rockfall magnitude and frequency, mountain infrastructure, mountain ecosystems, and tourism. The long-term trend in the thickening of the active layer and thus the subsidence of the permafrost table in the Swiss Alps due to climate warming is well observed and documented. However, less is known about how sub-seasonal to seasonal variability of atmospheric temperature, in particular individual multi-weekly heatwaves in summer, influence below-ground temperature from year to year. In this interdisciplinary study, we thus explore how atmospheric temperature variability on timescales of days to seasons affects the variability of below-ground temperature and the depth of the permafrost table, measured at various rock borehole stations of the Swiss Permafrost Monitoring Network PERMOS. In addition, we evaluate how well the snowpack and ground surface model SNOWPACK is able to reproduce this relationship. The insights from this analysis will pave the way to couple the SNOWPACK model to sub-seasonal to seasonal weather prediction models, which are increasingly being used to predict the probability of heatwave occurrence several weeks ahead. Such a coupling could allow for a prediction of the evolution of below-ground temperature and of significant permafrost anomalies on an operational basis, and thereby support early warning systems for alpine hazards.

Climate change affects the climatology of surface precipitation in spatially inhomogeneous ways, and it is challenging to identify and quantify the contribution of atmospheric circulation changes to this pattern. Various methods have been developed to characterize the large-scale atmospheric circulation and assess its changes, e.g., by classifying the flow into so-called weather regimes or circulation types. Several studies have then related frequency changes of these regimes due to global warming to changes in surface weather parameters. However, even without regime frequency changes, the climatology of surface parameters may change due to so-called regime intensity changes (e.g., a particular regime becomes on average wetter or drier). In this study, the question of how relevant frequency changes of weather regimes are for understanding climate change signals in surface precipitation is addressed with a novel conceptual framework. For every regime i, a spatially varying parameter gamma i(P) is introduced, which corresponds to the ratio of the contributions from regime frequency vs. regime intensity changes to the climate change signal of precipitation P. Conceptual considerations show that gamma i(P) is (i) proportional to the relative change of regime frequency, (ii) proportional to the regime-specific anomaly of precipitation, and (iii) inversely proportional to the climate change effect on regime intensity. The combination of these independent and competing factors makes the study of gamma i(P) interesting and insightful. As a specific example application of this framework, we consider a 7-category weather regime classification in the North Atlantic-European sector and large ensemble simulations with the CESM1 climate model under the RCP8.5 emission scenario for the periods 1990-1999 and 2091-2100. Considering gamma i(P) for surface precipitation, P in this simulation setup reveals that (1) gamma values are typically less than 0.25 and therefore, to first order, frequency changes of weather regimes (WRs) are of secondary importance for explaining climate change signals in P - in contrast, the intensity changes dominate, which are to a large degree, but not entirely, related to the so-called thermodynamic effects of global warming; (2) the main reason for the generally low values of gamma is the comparatively small WR frequency changes and the limited regime-specific anomalies of P, in particular over continental Europe; and (3) gamma values tend to be slightly larger for precipitation variables that are less constrained by thermodynamic arguments, i.e., gamma for the number of wet days is larger than gamma for the number of heavy-precipitation days. In summary, this study provides a generally applicable framework to quantify climate change effects of regime frequency changes on surface parameters, it illustrates the key conditions that must be fulfilled such that these frequency changes can become relevant, and, at least in our application, it shows that these conditions are generally not fulfilled.

Droughts and floods can occur simultaneously at the continental scale, resulting in impacts from both excess and deficit of water at the same time. Such spatially compounding drought-flood events can result in contrasting water management challenges. Despite their relevance for insurance and management, we know little about their occurrence, seasonality, and large-scale atmospheric drivers. We address this research gap by studying spatially compounding drought-flood events using streamflow and precipitation observations in Europe. Our results show that these compounding events have a strong seasonality and occur most often during winter, spring, and in June, even though their meteorological counterpart, spatially compounding dry-wet extremes, mainly occur in summer. Each of these events has its own spatial footprint. These footprints can be categorized in four main clusters, with the flood part of the compounding extreme either occurring over Central Europe or the UK in summer or winter, Eastern Europe in spring, or Southern Europe in winter. Our analysis of the relationship between seven European weather regimes and spatially compounding drought-flood events shows that these events are mainly favored by different types of blocking regimes in winter, spring, and summer and by the Zonal Regime in winter and spring. These weather regimes are all related to stable high-pressure systems located over one part of Europe and cyclonic conditions at their edges over another part of Europe. We conclude that spatially compounding drought-flood events are favored by particular weather regimes, whose relative importance depends on the season and the location of the flood hotspot.

Abstract. Extratropical cyclones are the main cause of high-impact weather events in the Mediterranean such as heavy precipitation, floods, severe winds, and dust storms. However, the accuracy in predicting the timing, location, and intensity of such events is often insufficient, which is typically related to errors in cyclone position, propagation, and intensity. In this two-part study we use operational forecasts from the ECMWF ensemble prediction system to quantify uncertainties in predicting high-impact weather conditions linked to Mediterranean cyclones. We apply an object-based approach to attribute Mediterranean cyclones to events of extreme precipitation and surface winds. In this first part, we introduce the probabilistic method and three illustrative case studies of Mediterranean cyclones that occurred between November 2022 and September 2023, including the infamous Storm Daniel as well as Storms Denise and Jan. We find that the cyclones as well as their attributed objects of extreme surface weather are predicted well for lead times ≤48 h. However, for longer lead times there is large case-to-case variability in the ensemble performance. Predictions of extreme surface weather objects are found to be more uncertain (i) for smaller and less coherent objects, (ii) if the attributed cyclone is captured by fewer ensemble members, and (iii) during the earlier stage of the cyclones' lifecycle.

Heatwaves pose a range of severe impacts on human health, including an increase in premature mortality. The summers of 2018 and 2022 are two examples with record-breaking temperatures leading to thousands of heat-related excess deaths in Europe. Some of the extreme temperatures experienced during these summers were predictable several weeks in advance by subseasonal forecasts. Subseasonal forecasts provide weather predictions from 2 weeks to 2 months ahead, offering advance planning capabilities. Nevertheless, there is only limited assessment of the potential for heat-health warning systems at a regional level on subseasonal timescales. Here we combine methods of climate epidemiology and subseasonal forecasts to retrospectively predict the 2018 and 2022 heat-related mortality for the cantons of Zurich and Geneva in Switzerland. The temperature-mortality association for these cantons is estimated using observed daily temperature and mortality during summers between 1990 and 2017. The temperature-mortality association is subsequently combined with bias-corrected subseasonal forecasts at a spatial resolution of 2-km to predict the daily heat-related mortality counts of 2018 and 2022. The mortality predictions are compared against the daily heat-related mortality estimated based on observed temperature during these two summers. Heat-related mortality peaks occurring for a few days can be accurately predicted up to 2 weeks ahead, while longer periods of heat-related mortality lasting a few weeks can be anticipated 3 to even 4 weeks ahead. Our findings demonstrate that subseasonal forecasts are a valuable-but yet untapped-tool for potentially issuing warnings for the excess health burden observed during central European summers.

Being able to predict meteorological droughts several weeks ahead would add value to many sectors including agriculture, river shipping as well as water and energy management. A commonly used meteorological drought index is the standardized precipitation index SPI-N, which puts precipitation anomalies of the past N months into a climatological perspective. The SPI correlates with anomalies of soil-moisture, streamflow or groundwater storage, and thus serves as an inexpensive and attractive hydrological proxy. In this study we quantify how well the SPI-N can be skillfully forecasted in Switzerland. Using ECMWF IFS extended-range forecasts quantile mapped from its native 36 km to a 2 km grid, we produce ensembles of SPI-N forecasts for the Swiss drought warning regions. While previous research has underlined the challenges faced by ensemble forecasting systems in accurately predicting daily precipitation in Europe beyond lead week 1, our analysis reveals that the skill of SPI-1, SPI-3, and SPI-6 forecasts extends into weeks 3 and 4. It generally holds that skill SPI-6 > skill SPI-3 > skill SPI-1. For example, we find that the skill of an SPI-3 forecast for week 4 is comparable to the skill of an SPI-1 forecast for week 2. Overall, the results indicate the potential for skillful prediction of meteorological drought on sub-seasonal timescales. We link the extended predictability horizon to the inherent characteristics of the SPI being a temporal aggregate: the SPI is less sensitive to the exact timing of precipitation events, while also retaining “memory” of past precipitation. The latter manifests in larger skill for longer accumulation time N, in which more observation are weighted into the forecasted SPI. Finally, we show how SPI forecasts and hydrological forecasts are devised as factors for the combined drought indicator, which forms the numerical basis of the new Swiss drought early warning system.

Extreme stratospheric polar vortex events, such as sudden stratospheric warmings (SSWs) or extremely strong polar vortex events, can have a significant impact on surface weather in winter. SSWs are most often associated with negative North Atlantic Oscillation (NAO) conditions, cold air outbreaks in the Arctic and a southward-shifted midlatitude storm track in the North Atlantic, while strong polar vortex events tend to be followed by a positive phase of the NAO, relatively warm conditions in the extratropics and a poleward-shifted storm track. Such changes in the storm track position and associated extratropical cyclone frequency over the North Atlantic and Europe can increase the risk of extreme windstorm, flooding or heavy snowfall over populated regions. Skillful predictions of the downward impact of stratospheric polar vortex extremes can therefore improve the predictability of extratropical winter storms on subseasonal timescales. However, there exists a strong inter-event variability in these downward impacts on the tropospheric storm track. Using ECMWF reanalysis data and reforecasts from the Subseasonal to Seasonal (S2S) Prediction Project database, we investigate the stratospheric influence on extratropical cyclones, identified with a cyclone detection algorithm. Following SSWs, there is an equatorward shift in cyclone frequency over the North Atlantic and Europe in reforecasts, and the opposite response is observed after strong polar vortex events, consistent with the response in reanalysis. However, although the response of cyclone frequency following SSWs with a canonical surface impact is typically captured well during weeks 1–4, less than 25 % of the reforecasts manage to capture the response following SSWs with a “non-canonical” impact. This suggests a possible overconfidence in the reforecasts with respect to reanalysis in predicting the canonical response after SSWs, although it only occurs in about two-thirds of the events. The cyclone forecasts following strong polar vortex events are generally more successful. Understanding the role of the stratosphere in subseasonal variability and predictability of storm tracks during winter can provide a key for reliable forecasts of midlatitude storms and their surface impacts.

Extratropical cyclones influence midlatitude surface weather directly via precipitation and wind and indirectly via upscale feedbacks on the large-scale flow. Biases in cyclone frequency and characteristics in medium-range to sub-seasonal numerical weather prediction might therefore hinder exploiting the potential predictability on these timescales. We thus, for the first time, identify and track extratropical cyclones in 21 years (2000 – 2020) of sub-seasonal ensemble reforecasts from the European Centre for Medium-Range Weather Forecasts (ECMWF) in the Northern Hemisphere in all seasons. Overall, the reforecasts reproduce the climatology of cyclone frequency and life-cycle characteristics qualitatively well up to six weeks ahead. However, there are significant regional biases in cyclone frequency, which can result from a complex combination of biases in cyclone genesis (locally and upstream), size, location, lifetime, and propagation speed. Their magnitude is largest in summer, with the strongest deficit of cyclones of up to 15% in the North Atlantic, relatively large in spring, and smallest in winter and autumn. Moreover, the reforecast cyclones are too deep in both ocean basins during most seasons, although intensification rates are captured well. An overestimation of cyclone lifetime and differences between the native spatial resolutions of the reforecasts and the verification dataset might explain this intensity bias in some cases, but there are likely further so far unidentified processes involved. While the patterns of cyclone frequency and life cycle biases often appear in lead time weeks 1 and 2, their magnitudes typically grow further at sub-seasonal lead times and, in some cases, saturate in weeks 5 and 6 only. Most of the dynamical sources of these biases thus likely appear in the early medium range, but biases on longer timescales probably contribute to their further increase with lead time. Our study provides a useful basis to identify, better understand, and ultimately reduce biases in the large-scale flow and in surface weather in sub-seasonal weather forecasts. Given the considerable biases during summer, when sub-seasonal predictions of precipitation and surface temperature will become increasingly important, this season deserves particular attention for future research.

Weather forecasts at subseasonal-to-seasonal (S2S) timescales have little forecast skill in the troposphere: individual ensemble members are mostly uncorrelated and span a range of atmospheric evolutions that are possible for the given set of external forcings. The uncertainty of such a probabilistic forecast is then determined by this range of possible evolutions - often quantified in terms of ensemble spread. Various dynamical processes can affect the ensemble spread within a given region, including extreme events simulated in individual members. For forecasts of geopotential height at 1000 hPa (Z1000) over Europe, such extremes are mainly comprised of synoptic storms propagating along the North Atlantic storm track. We use ECMWF reforecasts from the S2S database to investigate the connection between different storm characteristics and ensemble spread in more detail. We find that the presence of storms in individual ensemble members at S2S timescales contributes about 20 % to the total Z1000 forecast uncertainty over northern Europe. Furthermore, certain atmospheric conditions associated with substantial anomalies in the North Atlantic storm track show reduced Z1000 ensemble spread over northern Europe. For example, during periods with a weak stratospheric polar vortex, the genesis frequency of Euro-Atlantic storms is reduced and their tracks are shifted equatorwards. As a result, we find weaker storm magnitudes and lower storm counts, and hence anomalously low subseasonal ensemble spread, over northern Europe.

The projected increase in heatwave intensity and frequency will have far-reaching consequences for the human and natural environment of Switzerland. Two particularly important consequences are heat-related excess mortality in the low-lying areas and heat-related acceleration of climate-change-induced alpine permafrost thawing in high-elevation areas. The latter will potentially have far-reaching impacts on alpine hazards, ecosystems, infrastructure, and tourism. In this interdisciplinary project, we assess the potential of using subseasonal heatwave predictions as a basis for early warning systems for the above-mentioned sectors in Switzerland. For the health sector, we show that the (observation-based) statistical relationship between temperature and mortality in combination with downscaled subseasonal temperature forecasts can be used to predict mortality attributable to heat. We demonstrate that for two densely populated areas of Switzerland (Cantons of Zurich and Geneva) and two past hot summers (2018 and 2022) this system is able to predict individual heat-related mortality peaks up to two weeks ahead and anticipate longer-lasting periods of heat-related excess mortality up to four weeks in advance. For the alpine sector, we show that individual summer heatwaves can play an important role in accelerating permafrost thawing, even though the process is driven by long-term climate change. We demonstrate this with idealized sensitivity experiments with the SNOWPACK model (a physical model that predicts the evolution of the snowpack and the ground temperature below). They indicate that both the duration of heatwaves as well as their timing within an individual summer are important for the intensity of the ground warming in permafrost regions. In summary, this project demonstrates a large potential for using subseasonal heatwave predictions for early warning systems for the health sector. For the alpine sector, it highlights the potential importance of individual heatwaves for permafrost thawing and raises the question if subseasonal heatwave predictions could support monitoring and early warning systems in high-elevation areas in some way.

The subseasonal predictability of heatwaves in Europe is relatively well understood regarding prediction skill horizon and physical drivers of predictability. Despite this progress, few studies have translated subseasonal model output into skillful operational heatwave forecast products and end-user-tailored impact forecasts. These are substantial challenges, given the relatively high uncertainties and the flow-dependent skill inherent in subseasonal prediction. In this project, we aim to translate subseasonal model output from the European Centre for Medium-Range Weather Forecasts (ECMWF) into end-user-tailored heatwave forecast products for Switzerland. We first perform a detailed verification of average subseasonal (hindcast) prediction skill for temperature and heatwaves in Switzerland on different spatial and temporal aggregation scales. This analysis demonstrates a significant increase in subseasonal forecast skill with increasing temporal aggregation scales. We then analyze to what extent previously-studied local and remote drivers (such as dry soils, lower-frequency atmospheric modes, or sea surface temperature anomalies) manifest as “windows of forecast opportunity” for heatwave prediction in Switzerland and its subregions. These steps are performed with two sets of hindcasts – one with the native grid resolution and one that has been downscaled (and bias-corrected) to a higher resolution using quantile mapping. This postprocessing helps to quantify the added value of downscaling at subseasonal lead times. Finally, we present some ideas on how the gained knowledge on spatio-temporal and flow-dependent characteristics of skill could be translated into an operational subseasonal heatwave prediction system for Switzerland – a step that is closely linked to the challenging question of how much skill is enough skill for specific end-user applications.

In recent years, there has been growing evidence that latent heat release in midlatitude weather systems such as warm conveyor belts (WCBs) contributes significantly to the onset and maintenance of blocking anticyclones (blocked weather regimes). Still, numerical weather prediction (NWP) and climate models struggle to correctly predict and represent atmospheric blocking in particular over Europe. Here, we elucidate the representation of WCB activity in 20 years of extended winter (1997-2017) of European Centre for Medium-Range Weather Forecast's IFS reforecasts around the onset of blocking over Europe (EuBL) employing different perspectives. First, we show that the model struggles to predict EuBL onsets already at 10-14 days lead time in line with a misrepresentation of WCB activity in the ensemble mean. However, we also find cases with accurate EuBL forecasts even in pentad 4 (15-19 days). This subset of successful forecasts at extended-range lead times goes in line with accurate WCB forecasts over the North Atlantic several days prior to the blocking onset. Second, investigating the time-lagged relationship of blocking onset and WCB activity, we find that WCB activity over the North Atlantic emerges well prior to the onset of the block and that different pathways into EuBL exist in the reforecasts compared to reanalysis. Finally, we find indication of predictability associated with a Rossby wave train emerging from the North Pacific. Although our study can not disentangle the roles of intrinsic predictability limits and model deficiencies, we show that correct predictions of EuBL go along with distinct patterns of WCB activity. Warm conveyor belts (WCBs) are weather systems associated with low pressure systems which occur predominantly over the ocean regions of the midlatitudes. Several recent studies highlight the role of latent heat release due to cloud formation in WCBs for the development of long-lived high pressure systems. However, current weather prediction and climate models struggle to accurately predict these high pressure systems, particularly over Europe (EuBL). This study, based on 20 years (1997-2017) of forecast data, reveals challenges in predicting WCB activity together with the onset of EuBL, especially within a lead time of 10-14 days. Successful predictions at extended lead times (15-19 days) align with precise forecasts of WCB activity over the North Atlantic and North Pacific, indicating a connection between these regions. Examining the timing of the onset of EuBL and WCB activity, we show that WCB activity over the North Atlantic precedes EuBL. Additionally, we identify both correct and incorrect pathways leading to EuBL and suggest that predictability of EuBL may arise from specific atmospheric patterns, particularly related to Rossby waves in the North Pacific. Warm conveyor belt (WCB) activity around the onset of atmospheric blocking over Europe (EuBL) is analyzed in reanalysis and sub-seasonal reforecasts Correct WCB prediction provides a sub-seasonal window of forecast opportunity for EuBL onset Synoptic activity over the North Pacific supports the development of a teleconnection that affects EuBL onset

Heatwaves in Switzerland have various impacts on human health and ecosystems. Moreover, heat extremes might act as final triggers for high-Alpine hazards such as glacier break-offs and rockfalls, because they can accelerate the slowly growing disturbance of the Alpine permafrost layer due to climate warming. Heatwaves often occur concurrently with drought, which can impact agriculture, reduce lake and river shipping due to low water levels, and reduce nuclear power generation due to shortage of cooling water. As heatwaves have become and are expected to become even more common with climate change, it is crucial to predict their occurrence ahead of time and to issue warnings for stakeholders and the general public. The goal of this interdisciplinary project is, therefore, to assess the potential of heatwave prediction and warnings for Switzerland on timescales up to several weeks. The project consists of two parallel branches: on the one hand, we investigate if the forecast skill horizon for Switzerland can be extended by predicting heatwaves via statistical downscaling from larger-scale weather patterns compared to the prediction based on direct model output on a grid point level. On the other hand, we evaluate the potential benefits of early warning products for sectors that are directly linked to human lives and livelihoods. One focus is on predicting heat-related mortality by coupling a statistical temperature-mortality model to extended-range temperature forecasts. Another focus is on better understanding and predicting the penetration of heatwaves into Alpine glaciers, rocks, and sediments, which might ultimately support early-warning systems for heat-related high-Alpine hazards. The aim of our presentation is to introduce the multifaceted project in more detail and to provide some first results from the different branches.

The prediction skill of sub‐seasonal forecast models is evaluated for seven year‐round weather regimes in the Atlantic–European region. Reforecasts based on models from three prediction centers are considered and verified against weather regimes obtained from ERA‐Interim reanalysis. Results show that predicting weather regimes as a proxy for the large‐scale circulation outperforms the prediction of raw geopotential height. Greenland blocking tends to have the longest year‐round skill horizon for all three models, especially in winter. On the other hand, the skill is lowest for the European blocking regime for all three models, followed by the Scandinavian blocking regime. Furthermore, all models struggle to forecast flow situations that cannot be assigned to a weather regime (so‐called no regime), in comparison with weather regimes. Related to this, variability in the occurrence of no regime, which is most frequent in the transition seasons, partly explains the predictability gap between transition seasons and winter and summer. We also show that models have difficulties in discriminating between related regimes. This can lead to misassignments in the predicted regime during flow situations in which related regimes manifest. Finally, we document the changes in skill between model versions, showing important improvements for the ECMWF and NCEP models. This study is the first multi‐model assessment of year‐round weather regimes in the Atlantic–European domain. It advances our understanding of the predictive skill for weather regimes, reveals strengths and weaknesses of each model, and thus increases our confidence in the forecasts and their usefulness for decision‐making.