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Droughts are characterized by prolonged and severe deficits in precipitation that can extend in time, over a season, a year or more. They are confined to specific climatic zones but can manifest in both high and low rainfall regions. Contributing factors include temperatures, strong winds, low relative humidity, and the characteristics of rainfall. Drought events are characterized through indices that can be categorized based on the specific impacts they are associated with, such as meteorological, agricultural, or hydrological effects. Using such indices for drought characterization serves multiple purposes, including detection, assessment, and representation of drought conditions within a particular region. Seasonal precipitatio is essential for social and economic development and activities, hence. Reliable seasonal forecasts, especially regarding extreme precipitation events, become crucial for sectors like agriculture and insurance. Europe, and in particular the Mediterranean region, is expected to be considerably affected under climate change. The northern regions are anticipated to exhibit higher variability, increasing the risk of floods, while the southern areas may face decreased rainfall, prolonged dry spells, and intensified evaporation, potentially leading to more frequent drought occurrences. This research aims to evaluate the prediction skill for extreme drought events at the seasonal time-scale using the SPI and SPEI indices over the EURO-Mediterranean area. The use of SPEI also takes into account the effect of temperature on the water balance, given by the calculation of potential evapotranspiration within it, which can be crucial in a context of global warming. We consider the seasonal forecasts provided by the Copernicus multi-system and we use the Brier Skill Score metric for the assessment of the performance. The objective is to understand potential predictability factors of these indices within the study area. The results show a positive performance for most of the areas examined, between 60 and 80 percent of the entire area for both indices. This led us to investigate possible optimization strategies to increase the skill in the area. Using the multi-model approach we optimize the prediction skill obtaining considerable performance in forecasting drought conditions. Different multi-model strategies are compared, including the selection or aggregation of available forecasts to achieve the best overall performance in the area. We show that multi-model optimization can indeed provide valuable probabilistic predictions of seasonal drought events in many areas of the Euro-Mediterranean that could be useful for the decision-making process of the affected end users.
Monte et al. (Fri,) studied this question.