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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.
Αnagnostopoulou et al. (Fri,) studied this question.