In Europe, hailstorms are a major source of weather-related damages, and Northern Italy represents one of the areas at the greatest risk. Although several works have analysed severe hailstorms in the Mediterranean area, they are largely case-based and do not provide a systematic classification of the synoptic circulation regimes that favour them. In this study, we analysed summer hail events in Northern Italy over the period 2014-2023 using the satellite-based Microwave Cloud Classification dataset, which provides detection of hail >2 cm. A total of 122 events were identified and classified through Principal Component Analysis and k-means clustering, leading to three distinct spatial patterns. ERA5 reanalysis was used to characterize the associated large-scale circulation. Common features across all clusters include south-westerly flow in the mid-troposphere, upper-level divergence at 250 hPa, and enhanced mid-tropospheric moisture transport. Spatial pattern 1 is mainly linked to hail in north-eastern Italy, spatial pattern 2 to the western Po Plain and Ligurian Sea areas, and spatial pattern 3 to north-western Italy and the Alps. The pattern 3 is particularly distinct, showing stronger geopotential and temperature anomalies, with moisture transport between 600 and 800 hPa exceeding the 90th percentile of the decade. Comparing the sub-periods 2014-2018 and 2019-2023, we found both an increase in hail frequency and a larger contribution of Cluster 3. These results highlight the role of synoptic-scale variability in shaping hail risk in Northern Italy and suggest a shift toward circulation types more favourable for Alpine hail over the past years. The methodology and findings provide a framework for future analyses linking hail occurrence to large-scale climate variability and projections.

Elevation-Dependent Precipitation Change (EDPC) is increasingly recognised as a key feature of mountain climate change, yet its global characteristics and representation in climate models remain poorly understood. Here, we assess the ability of current-generation global climate models to capture EDPC and to provide projections of its future evolution. We analyse elevational trends in annual mean, heavy, and extreme daily precipitation for the historical (1951–2020) and future (2015–2099) periods across five mountain regions—Tibetan Plateau, US Rockies, Greater Alpine Region, northern Andes, and southern Andes—using 28 CMIP6 models and the ERA5 reanalysis as a reference. Model simulations are analysed using a clustering approach to identify groups of models exhibiting similar EDPC patterns. Results show inter-model variability, with cluster composition differing across regions and precipitation indices. During the historical period, distinct EDPC behaviours emerge: in some regions (e.g. the Tibetan Plateau), at least one cluster closely reproduces the ERA5 profile, whereas in others (e.g. the Andes and the US Rockies) no cluster matches ERA5. Projections reveal region- and cluster-dependent EDPC signals, precluding a robust characterisation of future EDPC. Models that best reproduce ERA5 EDPC behaviour do not constrain projected future responses. Nevertheless, cluster membership remains stable across emission scenarios (SSP2−4.5 and SSP5−8.5), with EDPC magnitude amplified under the higher-emission scenario, indicating modulation by climate change intensity. Finally, while the multi-model ensemble mean reproduces ERA5 reasonably well in the historical period, it tends to overestimate precipitation changes in projections, highlighting the value of clustering for assessing EDPC in climate model projections.