Severe and repeated droughts in Europe have significant impacts on agriculture, transport, energy and healthcare. During the summer of 2003, more than the two-thirds of Europe was under drought. The drought events of 2010 and 2018 were of a similar extent to 2003. An unprecedented stress on water levels throughout the entire EU was created by the combination of severe drought and heat waves during August 2022—the worst drought event in 500 years according to according to the Commission’s Joint Research Centre. A raised awareness of drought characteristics is essential for better drought forecasting and monitoring in order to provide reliable adaptation strategies for drought hazard. In this study, the drought over six European stations for the last three decades using the standardized precipitation index (SPI) and the standardized precipitation evapotranspiration index (SPEI) was analyzed. SPI reveals that there are no significant changes in dry and wet conditions, while SPEI shows a significant increase in the drought frequency during the last decades for Central Europe and the Mediterranean. The discrepancies between the two indices can be explained by the increasing temperature and evapotranspiration that are fundamental components of drought occurrence in Europe. The SPI12 index managed to identify the drought of August 2022 in many regions in Europe, but with less intensity than it was recorded. Conversely, SPEI12 was able to identify the intensity of the drought.

Eastern Europe experienced a pronounced warming trend in the summer of 2024, with average maximum temperature anomalies reaching 3.35 ^∘ C in June and 3.73 ^∘ C in July, and minimum temperature anomalies averaging 2.17 ^∘ C in June and 2.60 ^∘ C in July, significantly exceeding the 1991–2020 climatological baseline. Moreover, three distinct heatwave events were identified: 3–11 June (9 days), 17 June–1 July (15 days), and 6–21 July (16 days), highlighting the prolonged and intense nature of the heat stress. Seasonal forecasting systems (GCFS, SEAS5, and Météo-France) consistently demonstrated the capability to predict above-climatological-mean temperatures over the Eastern Mediterranean, particularly the Balkan Peninsula, showcasing robust regional warming trend capture, though with variations in accuracy across lead times. The accurate prediction of subtropical influences, as evidenced by consistent positive temperature anomaly forecasts over North Africa and the Iberian Peninsula, underscores the forecasting systems’ capacity to represent large-scale circulation patterns, yet the predictive skill for midlatitude blocking events and their spatial extent remains subject to limitations and model-specific variations. Despite the inherent limitations in long-range meteorological predictability, particularly concerning heatwave timing and location, the study emphasizes the critical imperative for continued refinement of seasonal forecasting models to enhance societal resilience against the escalating impacts of climate change, particularly through improved representation of synoptic atmospheric drivers.

Seasonal climate forecasts are an essential tool for providing early insight into weather-related impacts and supporting decision-making in sectors such as agriculture, energy, and disaster management. Accurate representation of atmospheric circulation at the seasonal scale is essential, especially in regions such as the Eastern Mediterranean, where complex synoptic patterns drive significant climate variability. The aim of this study is to perform a comparison of weather type classifications between ERA5 reanalysis and seasonal forecasts in order to assess the ability of seasonal data to capture the synoptic patterns over the Eastern Mediterranean. For this purpose, we introduce a regional seasonal forecasting framework based on the state-of-the-art Advanced Research WRF (WRF-ARW) model. A series of sensitivity experiments were also conducted to evaluate the robustness of the model’s performance under different configurations. Moreover, the ability of seasonal data to reproduce observed trends in weather types over the historical period is also examined. The classification results from both ERA5 and seasonal forecasts reveal a consistent dominance of anticyclonic weather types throughout most of the year, with a particularly strong signal during the summer months. Model evaluation indicates that seasonal forecasts achieve an accuracy of approximately 80% in predicting the daily synoptic condition (cyclonic or anticyclonic) up to three months in advance. These findings highlight the promising skill of seasonal datasets in capturing large-scale circulation features and their associated trends in the region.

Seasonal climate forecasts offer valuable insights into expected climate conditions up to several months in advance, supporting decision-making in sectors such as agriculture, energy, and disaster management. However, seasonal forecasts still struggle to capture the spatial and temporal variability of atmospheric circulation, particularly in regions prone to climate extremes. Improving their reliability requires better methods for assessing how well they reproduce key features of synoptic-scale dynamics. Classifying synoptic patterns allows for a more comprehensive statistical assessment of atmospheric circulation, enabling the identification of dominant weather regimes and their evolution over time.In this study we developed an updated approach to classify weather patterns in Eastern Mediterranean using z500 geopotential heights and vertical velocity data. The classification method identifies five anticyclonic and seven cyclonic types. Our findings indicate that during the cold season, cyclonic and anticyclonic weather types occur at similar frequencies, whereas anticyclonic types dominate in the summer. To evaluate the ability of seasonal forecast models to predict synoptic patterns up to three months in advance, we apply the classification to seasonal hindcast simulations produced by the state-of-the-art Advanced Research WRF (WRF-ARW) model for the period 1981-2016. The classified weather types from the seasonal model outputs are compared against those derived from ERA5 to assess forecast skill, biases, and limitations in capturing large-scale atmospheric circulation. Preliminary findings indicate that synoptic conditions in the study region can be reliably predicted three months in advance (lead time 3), with the seasonal model achieving an accuracy rate of over 75% in estimating the likelihood of anticyclonic or cyclonic conditions. This classification-based approach provides a process-oriented alternative for evaluating seasonal forecast performance.AcknowledgmentsThe work was supported by PREVENT project. This project has received funding from Horizon Europe programme under Grant Agreement No: 101081276.

The Mediterranean region is widely recognized as a climate change hotspot, where the increasing frequency and intensity of extreme phenomena pose significant threats to both environmental stability and socio-economic resilience. Rising temperatures, shifting precipitation patterns, and prolonged droughts have led to heightened risks of heatwaves, flash floods, and agricultural stress across the basin. Identifying the underlying atmospheric drivers of these events is critical for improving early warning systems and developing effective adaptation strategies. However, traditionally applied correlation methods fail to provide robust evidence regarding the physical causality behind the analyzed connections.Using the Peter and Clark momentary conditional independence (PCMCI) causal discovery approach, the current study examines the causal relationships between temperature (including mean temperature, and its minimum and maximum values), precipitation, and soil moisture in the studied region. This advanced approach allows for the quantification of robust causal links while accounting for multivariate dependencies and time lags. To account for regional climatic heterogeneity, the Mediterranean is partitioned into eight subregions using k-means clustering based on temperature and soil moisture profiles. The study evaluates the causal influence of large-scale teleconnection patterns and Synoptic Weather Types (WTs), during both summer and winter seasons.Results indicate a strong influence of Western patterns during winter, particularly the North Atlantic Oscillation (NAO). Furthermore, the analysis reveals an important influence from eastern drivers during the summer months, notably through the Indian Summer Monsoon and the Madden-Julian Oscillation (MJO). By quantifying the strength of these causal links and identifying the specific weather types that lead to adverse conditions, this research offers a more rigorous understanding of synoptic mechanisms than traditional correlation-based methods. These findings are crucial for enhancing seasonal forecasting and advancing climate resilience in the Mediterranean Basin.Acknowledgements: This research was supported by the PREVENT project that has received funding from the EU Horizon Europe framework programme (grant no. 101081276)

Seasonal forecasting refers to the prediction of weather conditions over one to several months, offering insights into temperature and precipitation anomalies relative to climatological means. Positioned between short-term weather forecasting and long-term climate projections, it supports critical decision-making in sectors such as agriculture and water resource management. Despite operational advances, forecast skill remains limited in many regions, particularly for precipitation, due to challenges in model resolution, data quality and the representation of complex climate dynamics. Improving seasonal forecast systems is essential to mitigate the impacts of climate variability and support sustainable development.The primary objective of this study is to identify the optimal combination of input datasets and cumulus convection parameterization schemes for conducting seasonal hindcast and forecast simulations using the Weather Research and Forecasting (WRF) model version 4.5. Sensitivity tests were conducted in the European domain at a horizontal resolution of 0.5° × 0.5°. A total of four simulations were carried out with changes in both the driving datasets that are used for the initial and boundary conditions, and the cumulus parameterization schemes, for the evaluation period 1998-2000. The simulations were forced by two different reanalysis products: the ECMWF Reanalysis version 5 (ERA5) and the NCEP Climate Forecast System Reanalysis (CFSR). Additionally, two cumulus parameterizations were utilized: the Grell-Freitas and the Kain-Fritsch scheme. The different simulated data were assessed to ensure the model’s robust performance. The evaluation of the simulations was performed for both temperature and total precipitation using the Climatic Research Unit (CRU) high-resolution gridded dataset.The results showed that WRF simulates temperature quite satisfactorily in the ERA5-driven simulations, and especially in the one with Kain-Fritsch scheme, while the CFSR-driven simulations highly deviate from the gridded data. Regarding total precipitation – a rather challenging parameter that is difficult to predict in numerical weather models as it depends on many different atmospheric conditions – WRF appear to overestimate this parameter in all simulations during winter, with the overestimation being more enhanced in mountainous areas. On the contrary, during summer total precipitation is mainly underestimated in the CFSR-driven simulations, while an enhanced overestimation is observed in mountainous areas (and especially the Alps) in all four simulations. These findings suggest that using ERA5 as forcing data in conjunction with the Kain-Fritsch cumulus scheme provides a more reliable performance of WRF model for seasonal simulations in the area of study.AcknowledgmentsThe work was supported by PREVENT project. This project has received funding from Horizon Europe programme under Grant Agreement No: 101081276.