Detection and attribution of changes in the frequency and intensity of extreme weather events have gained increasing attention in recent years, driven by the need to better understand the effects of climate change, communicate risks to the public, and assess socioeconomic impacts. However, convective extremes—such as large hail—remain understudied in this context, primarily due to the inability of numerical models to directly simulate them given their small spatiotemporal scales and the involved physical processes. Northern Italy is a known hotspot for severe hailstorms, including giant hail (diameter > 8 cm), with recent studies suggesting a rising trend in such events, posing significant economic consequences. In this work, we present a methodology for detection and attribution of trends in both frequency and intensity of giant hail events. Using a database of 26 cases recorded across the region between 2018 and 2024 and ERA5 reanalysis data, we identify the atmospheric circulation patterns associated with these phenomena and employ circulation analogs to analyze trends over the 1950–2024 period. This approach allows us to evaluate circulation-conditioned trends in dynamic and thermodynamic parameters linked to convective extremes—and thus giant hail potential—over the entire reanalysis period, for most of which we do not have reports of giant hail events. The most pronounced trend is a marked increase in CAPE (Convective Available Potential Energy) across analogs for nearly all events in the dataset. Contrary to unconditioned studies, bulk wind shear does not decrease but instead shows a systematic (though mostly non-significant) rise. A significant increase in 200 hPa divergence is also observed, though it remains unclear whether this is a driver or a consequence of enhanced convective intensity. These preliminary results highlight the growing potential for high-impact convective events under ongoing climate change. Furthermore, by analysing the synoptic-scale trends, this study offers a robust framework for anticipating future giant hail risk, which is particularly valuable for informing adaptation strategies.
Pons et al. (Fri,) studied this question.