Towards a reliable assessment of climate change impact on droughts in Southern Italy: Evaluation of EURO-CORDEX historical simulations by high-quality observational datasets

Abstract

Abstract. Many recent studies indicate climate change as a phenomenon that significantly alters the water cycle in different regions worldwide, also implying new challenges in water resources management and drought risk assessment. To this end, it is of key importance to ascertain the quality of Regional Climate Models (RCMs), which are commonly used for assessing at proper spatial resolutions future impacts of climate change on hydrological events. In this study, we propose a statistical methodological framework to assess the quality of the EURO-CORDEX RCMs concerning their ability to simulate historic climate (temperature and precipitation) and drought characteristics (duration, accumulated deficit, and intensity) determined by the theory of runs, at seasonal and annual time scales, by comparison with high-density and high-quality ground-based observational datasets. In particular, the proposed methodology is applied to Sicily and Calabria regions (Southern Italy), where long historical precipitation and temperature series were recorded by the ground-based monitoring networks operated by the formerly Regional Hydrographic Offices, whose density is considerably greater than observational gridded datasets available at the European level, such as E-OBS. Results show that the more skilful models, able to reproduce, overall, precipitation and temperature variability, as well as drought characteristics, are based on the COSMO-CLM RCM, with the significant exception of the combination based on the HadGEM2-ES GCM and the RACMO RCM. Nevertheless, the choice of the most appropriate model depends on the specific variable analysed, as well as the temporal and spatial scale of interest. From this point of view, the proposed methodology highlights the skills and weaknesses of the different configurations, supporting a proper model selection for climate projections depending on the examined hydrologic processes.