One of the impacts of climate change is an increase in the frequency and intensity of extreme rainfall events. This has very significant social and economic consequences for the affected areas (flooding, loss of life, destruction of infrastructure, etc.). Future trends indicate a further increase in extreme rainfall in the second half of the century, making the need for the timely and accurate forecasting of these phenomena more urgent than ever. However, despite the technological development of weather and climate models in recent years, there are still limitations in detecting the extremes, especially regarding the precipitation parameter. Extreme precipitation events show a link with temperature. The Clausius–Clapeyron (CC) equation, which relates temperature to saturation vapor pressure (e_s), is used to study the sensitivity of precipitation to temperature increase because it can estimate the increase in the available atmospheric water vapor with respect to temperature. Focusing on the Thessaloniki region in Greece, the aim of this paper is to investigate the applicability of the Clausius–Clapeyron relation to the scaling relationship between extreme precipitation intensity and surface air temperature. An additional attempt is also made to test the possibility of improving the underestimation that the reanalysis models exhibit in recording the extremes and in particular the ERA5 Land dataset.

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.

It is well-established that the Mediterranean region is a climate change hotspot, with extreme phenomena increasing all across the basin. Heatwaves have intensified both in frequency and duration, resulting in unprecedented high temperatures that exacerbate health risks and strain energy resources. The region has also experienced a rise in intense precipitation events, leading to flash floods that pose significant threats to human lives, infrastructure, and agricultural yields. Moreover, the Mediterranean is witnessing a notable increase in the frequency and severity of wildfires, propelled by prolonged droughts and exacerbated by shifting precipitation patterns. These climatic extremes not only endanger the safety and well-being of local communities but also pose tough challenges to regional economies, which are heavily reliant on sectors such as agriculture and tourism. A key step in addressing extreme events is the timely prediction of these phenomena by identifying the key drivers that lead to their occurrence. In our analysis, we aim to pinpoint the fundamental drivers behind extremes in the Mediterranean. The MED-HOT index, designed to evaluate regional climate extremes through concurrent analysis of changes in precipitation and temperature frequency and intensity, provides a comprehensive assessment of Mediterranean climate challenges, highlighting areas requiring immediate attention and intervention. Using the MED-HOT index for the identification of the extreme hot spot regions, we uncover the potential drivers and the associated time lags of extreme precipitation, temperature, and drought. We apply the Peter and Clark momentary conditional independence (PCMCI) causal discovery algorithm to identify the prominent atmospheric teleconnection patterns driving extreme weather events in the Mediterranean Basin. The study suggests that teleconnection patterns in the North Atlantic notably impact precipitation, especially in the western parts of the Mediterranean Basin. Additionally, it is observed that the impact of teleconnections on extreme temperatures is more pronounced during the summer season. The results of this study are crucial for understanding the underlying drivers of extremes weather events and enhancing our preparedness to adapt to the impacts of climate change.

Climate change is a crucial issue of the 21st century, leading to more frequent and severe extreme precipitation events globally. These events result in significant social and economic disruptions, including flooding, loss of life, and damage to infrastructure. Projections suggest that extreme rainfall will intensify in the latter half of the century, underscoring the need for accurate and timely forecasting. Despite advancements in meteorological and climate models that offer high accuracy for various weather parameters, these models still struggle to detect extreme values, particularly for precipitation. This research examines the sensitivity of extreme precipitation events to temperature, based on the Clausius-Clapeyron relationship, focusing on Thessaloniki, Greece. It also evaluates the effectiveness of reanalysis data in identifying extreme precipitation and explores how rainfall-temperature relationships can enhance prediction accuracy. The findings are vital for improving the estimation of extreme rainfall events and informing the design of flood-resilient infrastructure.

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.

Climate change has a noticeable impact on the Mediterranean region, causing changes in the seasonal patterns and yearly variations of important meteorological variables along with the occurrence, strength, duration, and timing of unusual and extreme events. While the Mediterranean is recognized as one of the prominent global hot spot for climate impacts, it's imperative to dissect its sub-regions more meticulously. These areas exhibit significant meteorological shifts due to climate change and merit classification as unique hotspots. The Mediterranean Hotspot Index (MED-HOT index) is utilized to identify hotspot sub-regions in the Mediterranean region, followed by a detailed assessment of the seasonal data on these regions.MED-HOT index is designed to assess regional climate extremes by simultaneously analyzing changes in frequency and intensity of key climatic variables, specifically precipitation and temperature. The MED-HOT index offers a comprehensive assessment of the major climate challenges in the Mediterranean, including heatwaves, extreme rainfall, and drought events, pinpointing regions requiring necessitating immediate attention and intervention . It calculated utilizing ERA5 daily maximum and minimum temperatures as well as precipitation data spanning into two time periods between 1981 to 2020 across 30 Mediterranean subregions. The analysis of the MED-HOT index reveals that Mediterranean hotspot region identification primarily relied on changes in the frequency of extremes, as the contribution of intensity changes was less important. Notably the change in extreme maximum temperature appears to play a crucial role in the most subregions. On an annual basis, the primary hot spots in the Mediterranean are identified as northern Greece, southern Italy, and the southeastern Mediterranean.Moreover, this study examined the performance of ECMWF-SEAS5 in predicting extreme temperature and precipitation in the Mediterranean hot spot regions. It assessed the quality of seasonal temperature and precipitation forecasts over the subregions by analyzing predictions from all members of the hindcasts and forecasts ensemble across the two seasons (winter and summer). The analyses included seasonal mean fields, anomaly correlations, statistical indicators, and seasonality index. ECMWF-SEAS5 successfully replicated the extreme precipitation anomalies observed in recent decades, with effectiveness varying based on lead time and subregion. Nevertheless, the system exhibited only moderate predictive ability in regions with medium and low predictability.AcknowledgmentsThe work was supported by PREVENT project. This project has received funding from Horizon Europe programme under Grant Agreement No: 101081276.