The Mediterranean region is an area particularly susceptible to water scarcity and drought. In this work, drought has been analyzed in Italy using multiple timescales of the standardized precipitation index (SPI) evaluated from the Integrated Multi-satellitE Retrievals for Global Precipitation Measurement product from 2000 to 2020. In particular, drought characteristics (severity, duration, and intensity) have been estimated by means of the run theory applied to the SPI values calculated in 3325 grid points falling within the Italian territory. Results clearly indicate that although a high number of drought events has been identified for the short timescale, these events present a lower duration and lesser severity than the long-timescale droughts. The main outcomes of this study, with the indication of the spatial distribution of the drought characteristics in Italy, allow identifying the areas that could also face water stress conditions in the future, thus requiring drought monitoring and adequate adaptation strategies.

This paper discusses the relative performance of several climate models in providing reliable forcing for hydrological modeling in six representative catchments in the Mediterranean region. We consider 14 Regional Climate Models (RCMs), from the EU-FP6 ENSEMBLES project, run for the A1B emission scenario on a common 0.22° (about 24 km) rotated grid over Europe and the Mediterranean region. In the validation period (1951 to 2010) we consider daily precipitation and surface temperatures from the observed data fields (E-OBS) data set, available from the ENSEMBLES project and the data providers in the ECA&D project. Our primary objective is to rank the 14 RCMs for each catchment and select the four best-performing ones to use as common forcing for hydrological models in the six Mediterranean basins considered in the EU-FP7 CLIMB project. Using a common suite of four RCMs for all studied catchments reduces the (epistemic) uncertainty when evaluating trends and climate change impacts in the 21st century. We present and discuss the validation setting, as well as the obtained results and, in some detail, the difficulties we experienced when processing the data. In doing so we also provide useful information and advice for researchers not directly involved in climate modeling, but interested in the use of climate model outputs for hydrological modeling and, more generally, climate change impact studies in the Mediterranean region.

Skills in reproducing monthly rainfall over Calabria (southern Italy) have been validated for the Climate Hazards group InfraRed Precipitation with Station data (CHIRPS) satellite data, the E-OBS dataset and 13 Global Climate Model-Regional Climate Model (GCM-RCM) combinations, belonging to the ENSEMBLES project output set. To this aim, 73 rainfall series for the period 1951–1980 and 79 series for the period 1981–2010 have been selected from the database managed by Multi-Risk Functional Centre of the Regional Agency for Environmental Protection (Regione Calabria). The relative mean and standard deviation errors, and the Pearson correlation coefficient have been used as validation metrics. Results showed that CHIRPS satellite data (available only for the 1981–2010 validation period) and RCMs based on the ECHAM5 Global Climate performed better both in mean error and standard deviation error compared to other datasets. Moreover, a slight appreciable improvement in performance for all ECHAM5-based models and for the E-OBS dataset has been observed in the 1981–2010 time-period. The whole validation-and-assessment procedure applied in this work is general and easily applicable where ground data and gridded data are available. This procedure might help scientists and policy makers to select among available datasets those best suited for further applications, even in regions with complex orography and an inadequate amount of representative stations.

A Linear Model (Smith and Barstad, 2004) was used to dynamically downscale Orographic Precipitation over western Norway from twelve General Circulation Model simulations. The GCM simulations come from the A1B emissions scenario in IPCC's 2007 AR4 report. An assessment of the changes to future Orographic Precipitation (time periods: 2046–2065 and 2081–2100) versus the historical control period (1971–2000) was performed. Results showed increases in the number of Orographic Precipitation days and in Orographic Precipitation intensity. Extreme precipitation events, as defined by events that exceede the 99.5%-ile threshold for intensity for the considered period, were found to be up to 20% more intense in future time periods when compared to 1971–2000 values. Using station-based observations from the control period, the results from downscaling could be used to generate simulated precipitation histograms at selected stations. The Linear Model approach also allowed for simulated changes in precipitation to be disaggregated according to their causal source: (a) the role of topography and (b) changes to the amount of moisture delivery to the site. The latter could be additionaly separated into moisture content changes due to the following: (i) temperature, (ii) wind speed, and (iii) stability. An analysis of these results suggested a strong role of moist stability and warming in the increasing intensity of extreme Orographic Precipitation events in the area.