A dataset of 54 daily Italian downward surface solar radiation (SSR) records has been set up collecting data for the 1959–2013 period. Special emphasis is given to the quality control and the homogenization of the records in order to ensure the reliability of the resulting trends. This step has been shown as necessary due to the large differences obtained between the raw and homogenized dataset, especially during the first decades of the study period. In addition, SSR series under clear-sky conditions were obtained considering only the cloudless days from corresponding ground-based cloudiness observations. Subsequently, records were interpolated onto a regular grid and clustered into two regions, northern and southern Italy, which were averaged in order to get all-sky and clear-sky regional SSR records. Their temporal evolution is presented, and possible reasons for differences between all-sky and clear-sky conditions and between the two regions are discussed in order to determine to what extent SSR variability depends on aerosols or clouds. Specifically, the all-sky SSR records show a decrease until the mid-1980s (dimming period), and a following increase until the end of the series (brightening period) even though strength and persistence of tendencies are not the same in all seasons. Clear-sky records present stronger tendencies than all-sky records during the dimming period in all seasons and during the brightening period in winter and autumn. This suggests that, under all-sky conditions, the variations caused by the increase/decrease in the aerosol content have been partially masked by cloud cover variations, especially during the dimming period. Under clear sky the observed dimming is stronger in the south than in the north. This peculiarity could be a consequence of a significant contribution of mineral dust variations to the SSR variability.

Snow represents a fundamental water resource for mountain and lowland areas. Changes in the frequency and magnitude of snow droughts can significantly impact societies and ecosystems that rely on snowmelt to satisfy their water demands. Here we documented and quantified the snow drought that affected the Italian Alps during the early 2020s. We used 15 long-term snow-depth series (period 1930–2023, elevation range: 864–2200 m a.s.l.) to simulate the snow water equivalent (SWE), in conjunction with climatic reanalysis data and river discharge observations. We found that the March SWE anomaly in 2022 reached the lowest value in the last century, due to an unprecedented combination of drier- and warmer-than-normal conditions in the period December 2021–March 2022. This event contributed to causing critical hydrological conditions in the Po and Adige rivers which, during summer 2022, experienced the worst hydrological drought ever recorded. Despite its unprecedented magnitude, the snow drought in 2022 is part of a recent pattern of increased intensity and frequency of snow-drought events since the 1990s, due to the combined increasing occurrence of warmer- and drier-than-normal climatic conditions during the snow season. Remarkably, three out of the five most severe snow-drought events occurred in the last five years, with exceptional snow-drought conditions even occurring in the last two consecutive winters, 2022 and 2023. The snow-drought conditions that occurred in the early 2020s in the Italian Alps warn of the pressing need for the implementation of impact mitigation measures to adapt to the fast changing snow and climatic conditions.

A data set of quality checked daily sunshine duration measurements was collected from 104 Italian sites over the 1936 to 2013 period. Monthly mean values were homogenized, projected onto a grid, and subjected to principle component analysis, which identified two significantly different regions: North and South. Sunshine duration temporal evolution is presented, and possible reasons for differences between the two regions are discussed in the light of a comparison with the trends found in observations of total cloud cover and with results from two neighboring regions: the Alps and Spain. In addition, trends for irradiance records, estimated from sunshine duration records using the Ångström‐Prescott formula, are presented too. The major feature of the trends, an increase in sunshine duration from the mid‐1980s, was common to both northern and southern Italy; the decrease in the preceding 30 year period was not, as northern Italy had a lower rate of decrease than southern Italy. The few records available during the earliest period of the data set indicate that sunshine duration in Italy increased from the mid‐1930s to the mid‐1950s. The further steps needed to identify and quantify the mechanisms giving rise to the observed trends and to the reported regional differences in dimming and brightening are outlined.

Snow cover is particularly important in the Alps for tourism and the production of hydroelectric energy. In this study, we investigate the spatiotemporal variability in three snow cover metrics, i.e., the length of season (LOS), start of season (SOS) and end of season (EOS), obtained by gap-filling of MOD10A1 and MYD10A1, daily snow cover products of MODIS (Moderate-resolution Imaging Spectroradiometer). We analyze the period 2000–2019, evaluate snow cover patterns in the greater Alpine region (GAR) as a whole and further subdivide it into four subregions based on geographical and climate divides to investigate the drivers of local variability. We found differences both in space and time, with the northeastern region having generally the highest LOS (74 ± 4 days), compared to the southern regions, which exhibit a much shorter snow duration (48/49 ± 2 days). Spatially, the variability in LOS and the other metrics is clearly related to elevation (r² = 0.85 for the LOS), while other topographic (slope, aspect and shading) and geographic variables (latitude and longitude) play a less important role at the MODIS scale. A high interannual variability was also observed from 2000 to 2019, as the average LOS in the GAR ranged between 41 and 85 days. As a result of high variability, no significant trends in snow cover metrics were seen over the GAR when considering all grid cells. Considering 500-m elevation bands and subregions, as well as individual grid points, we observed significant negative trends above 3000 m a.s.l., with an average of −17 days per decade. While some trends appeared to be caused by glacierized areas, removing grid cells covered by glaciers leads to an even higher frequency of grid cells with significant trends above 3000 m a.s.l., reaching 100% at 4000 m a.s.l. Trends are however to be considered with caution because of the limited length of the observation period.

Surface air temperature (t2m) data are essential for understanding climate dynamics and assessing the impacts of climate change. Reanalysis products, which combine observations with retrospective short‐range weather forecasts, can provide consistent and comprehensive datasets. ERA5 represents the state‐of‐the‐art in global reanalyses and supplies initial and boundary conditions for higher‐resolution regional reanalyses designed to capture finer‐scale atmospheric processes. However, these products require validation, especially in complex terrains like Italy. This study analyses the capability of different reanalysis products to reproduce t2m fields over Italy during the 1991–2020 period. The analyses encompass ERA5, ERA5‐Land, the MEteorological Reanalysis Italian DAtaset (MERIDA), the Copernicus European Regional ReAnalysis (CERRA), and the Very High‐Resolution dynamical downscaling of ERA5 REAnalysis over ITaly (VHR‐REA_IT). The validation we conduct pertains to both the spatial distribution of 30‐year seasonal and annual normal values and the daily anomaly records. Each reanalysis is compared with observations projected onto its respective grid positions and elevations, overcoming any model bias resulting from an inaccurate representation of the real topography. Key findings reveal that normal values in reanalyses closely match observational values, with deviations typically below 1°C. However, in the Alps, winter cold biases sometimes exceed 3°C and show a relation with the elevation. Similar deviations occur in the Apennines, Sicily, and Sardinia. Conversely, VHR‐REA_IT shows a warm bias in the Po Valley up to 3°C in summer. Daily anomalies generally exhibit lower errors, with MERIDA showing the highest accuracy and correlation with observational fields. Moreover, when aggregating daily anomalies to annual time scales, the errors in the anomaly records rapidly decrease to <0.5°C. The results of this study empower reanalysis users across multiple sectors to gain a more profound insight into the capabilities and constraints of different reanalysis products. The knowledge and the characterization of the reanalyses t2m bias against observations can indeed be crucial when incorporating these products into their research and practical applications.