Maria Pyrina

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.

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.

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.

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.

Subseasonal forecasts provide valuable guidance for decision-making but remain limited by systematic biases and coarse spatial resolution, particularly over regions with complex orography. Quantile mapping (QM) is widely used for post-processing because it enables effective bias correction and downscaling, yet it applies a distributional correction and therefore neglects the case-specific dependence between forecasts and observations. To move toward a more integrated approach, we explore a diffusion-based generative framework for simultaneous probabilistic bias correction and high-resolution downscaling, with lead time explicitly included as a conditioning variable. We employ a conditional denoising diffusion model (DM) trained on high-resolution gridded observations (2 km) and conditioned on coarse-resolution ensemble-mean subseasonal forecasts (36 km) from the European Centre for Medium-Range Weather Forecasts (ECMWF) Integrated Forecasting System (IFS) subseasonal system. We focus here on a univariate temperature model and on training strategies. By learning conditional distributions of observations given forecasts and lead time, diffusion-based generative models enable case-dependent corrections that exploit forecast–observation relationships. Two training configurations are examined to assess how lead-time conditioning and data availability influence the learning of biases. Results show that the DM recovers realistic fine-scale temperature patterns and achieves meaningful lead-time-dependent bias correction across subseasonal lead times, even when trained on relatively limited datasets. However, compared to QM, the diffusion-based forecasts exhibit lower skill in probabilistic metrics such as the continuous ranked probability score (CRPS) and discrimination for extremes. Overall, this work represents a first step toward lead-time-aware subseasonal post-processing using DMs and highlights directions for future methodological development.

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.

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.

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 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.

Understanding and characterizing temperature extremes is essential for assessing climate impacts and risks. Robust statistical analysis of such extremes requires large datasets, yet observational records often provide limited samples of rare events. Hindcasts, i.e., retrospective forecast model runs for past dates, are typically used to correct model biases, but their potential for extreme event analysis remains underexplored. Approaches such as UNSEEN (UNprecedented Simulated Extremes using Ensembles) have investigated the potential of seasonal hindcast ensembles to provide large samples of events that are physically plausible, particularly for assessing rare events. However, seasonal hindcasts often focus on monthly means.In this study, we explore whether a similar approach can be applied to subseasonal hindcasts, evaluating their potential to serve as alternative realizations of extreme events at daily resolution. We use two complementary methods to compare global temperature extremes in ECMWF subseasonal hindcast with ERA-5 reanalysis: (1) the statistical upper bound of daily 2-meter temperature, and (2) the probability of record-breaking daily 2-meter temperature. By leveraging existing subseasonal hindcast ensembles, we aim to evaluate whether these datasets can be repurposed to study temperature extremes that have not yet been observed but are plausible under current climate conditions.