The Mediterranean region is one of the most responsive areas to climate change and was identified as a major “hot-spot” based on global climate change analyses. This study provides insight into local climate changes in the Mediterranean region under the scope of the InTheMED project, which is part of the PRIMA programme. Precipitation and temperature were analyzed in an historical period and until the end of this century for five pilot sites, located between the two shores of the Mediterranean region. We used an ensemble of 17 Regional Climate Models, developed in the framework of the EURO-CORDEX initiative, under two Representative Concentration Pathways (RCP4.5 and RCP8.5). Over the historical period, the temperature presents upward trends, which are statistically significant for some sites, while precipitation does not show significant tendencies. These trends will be maintained in the future as predicted by the climate models projections: all models indicate a progressive and robust warming in all study areas and moderate change in total annual precipitation, but some seasonal variations are identified. Future changes in droughts events over the Mediterranean region were studied considering the maximum duration of the heat waves, their peak temperature, and the number of consecutive dry days. All pilot sites are expected to increase the maximum duration of heat waves and their peak temperature. Furthermore, the maximum number of consecutive dry days is expected to increase for most of the study areas.

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This study provides an up-to-date analysis of climate change over the Salento area (southeast Italy) using both historical data and multi-model projections of Regional Climate Models (RCMs). The accumulated anomalies of monthly precipitation and temperature records were analyzed and the trends in the climate variables were identified and quantified for two historical periods. The precipitation trends are in almost all cases not significant while the temperature shows statistically significant increasing tendencies especially in summer. A clear changing point around the 80s and at the end of the 90s was identified by the accumulated anomalies of the minimum and maximum temperature, respectively. The gradual increase of the temperature over the area is confirmed by the climate model projections, at short—(2016–2035), medium—(2046–2065) and long-term (2081–2100), provided by an ensemble of 13 RCMs, under two Representative Concentration Pathways (RCP4.5 and RCP8.5). All the models agree that the mean temperature will rise over this century, with the highest increases in the warm season. The total annual rainfall is not expected to significantly vary in the future although systematic changes are present in some months: a decrease in April and July and an increase in November. The daily temperature projections of the RCMs were used to identify potential variations in the characteristics of the heat waves; an increase of their frequency is expected over this century.

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Missing data is a frequent problem in meteorological and hydrological temporal observation data sets. Finding effective solutions to this problem is essential because complete time series are required to conduct reliable analyses. This study used daily rainfall data from 60 rain gauges spatially distributed within Portugal's Guadiana River basin over a 30-year reference period (1976–2005). Gap-filling approaches using kriging-based interpolation methods (i.e. ordinary kriging and simple cokriging) are presented and compared to a deterministic approach proposed by the Food and Agriculture Organization (FAO method). The suggested procedure consists of fitting monthly semi-variogram models using the average daily rainfall from all available meteorological stations for each month in a reference period. This approach makes it possible to use only 12 monthly semi-variograms instead of one for each day of the gap period. Ordinary kriging and simple cokriging are used to estimate the missing daily precipitation using the semi-variograms of the month of interest. The cokriging method is applied considering the elevation data as the secondary variable. One year of data were removed from some stations to assess the efficacy of the proposed approaches, and the missing precipitation data were estimated using the three procedures. The methods were validated through a cross-validation process and compared using different performance metrics. The results showed that the geostatistical methods outperformed the FAO method in daily estimation. In the investigated study area, cokriging did not significantly improve the estimates compared to ordinary kriging, which was deemed the best interpolation method for a large majority of the rainfall stations.

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<p>Ongoing climate change makes both short- and long-term adaptation and mitigation strategies urgently needed. While many long-term climate models have been developed and investigated in recent years, little attention has been paid to short-term simulations. The first attempts to perform multi-model initialized decadal forecasts were presented in the fifth Coupled Model Intercomparison Project 5 (CMIP5). Near-term climate prediction models are new socially relevant tools to support the decision makers delivering climate adaptation solutions on an annual or decadal scale. Recent improvements in decadal models were coordinated in CMIP6 and the World Climate Research Program (WCRP) Grand Challenge on Near Term Climate Prediction, as part of the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (AR6, IPCC). The Decadal Climate Prediction Project (DCPP) provides decadal climate forecasts based on advanced techniques for the reanalysis of climate data, initialization methods, ensemble generation and data analysis. The initialization allows to consider the predictability of the internal climate variability reducing the prediction errors compared to those of the long-term projections, whose simulations do not take into account the phasing between the internal variability of the model and the observations. The aim of this work is to assess the near-future climate change in the Emilia-Romagna region in northern Italy until 2031. The hydrological variables analyzed are the daily precipitation and maximum and minimum temperature. An ensemble of models, with the highest resolution available, is used to handle the uncertainty in the predictions. Initially, to assess the reliability of the selected climate models, the hindcast data of the DCPP are checked against observations. Then, the DCPP predictions are used to investigate the variability of precipitation and temperature in the near future over the investigated area. Some climate features that are referenced to have an important impact on human health and activities are evaluated, such as drought indices and heat waves.</p>