This study investigates changes in extreme hourly precipitation across Italy using a high-resolution reanalysis, a dataset that combines observations and weather models to reconstruct past atmospheric conditions. By analysing over 35 years of hourly data, the study identifies an increase in extreme precipitation events in Alpine areas during summer and southern coastal regions in autumn, providing insights into evolving precipitation patterns and supporting climate resilience planning.

Understanding past climates requires data extending beyond modern instrumental records. This study shows that blue intensity (BI) measurements from European larch trees in the Southern Rhaetian Alps provide a stronger proxy for reconstructing past summer temperatures than traditional tree-ring-width data. BI processing enables regional-scale reconstructions and helps extend these reconstructions to the Mediterranean Basin and northern Europe, with excellent correlations to existing data.

We studied temporal trends and variability of snow depth and snow water equivalent (SWE) in six regions of the Italian Alps. We applied different statistical analyses to a dataset of homogeneous and continuous measurements of snow depth and SWE, temporally spanning from 1967 to 2020, and discussed the results with meteo-climatic data. Our results quantify the decrease of SWE in the study area, confirming the impacts of climate modifications on the cryosphere in the Alps.

We use a new satellite dataset to study the spatiotemporal evolution of surface melting over Greenland at an enhanced resolution of 3.125 km. Using meteorological data and the MAR model, we observe that a dynamic algorithm can best detect surface melting. We found that the melting season is elongating, the melt extent is increasing and that high-resolution data better describe the spatiotemporal evolution of the melting season, which is crucial to improve estimates of sea level rise.

Flood risk is an increasing threat to urban communities and their strategical assets worldwide. Non-structural practices, such as emergency management plans, can be effective in order to decrease the flood risk in strongly urbanized areas. Mobile phone data provide reliable estimates of the spatiotemporal variability in people exposed to flooding, thus enhancing the preparedness of stakeholders involved in flood risk management. Further, practical advantages emerge with respect to crowdsourcing.