A modelled multi-decadal hailday time series for Switzerland

Abstract. In Switzerland, hail is one of the costliest natural hazards, causing extensive damage to agriculture, cars, and infrastructure each year. In a warming climate, hail frequency and its patterns of occurrence are expected to change, which is why understanding the long-term variability and its drivers is essential. Therefore, this study presents new multidecadal daily hail time series for Northern and Southern Switzerland from 1959 to 2022. Daily radar hail proxies and environmental predictor variables from ERA-5 reanalysis are used to build an ensemble statistical model for predicting past hail occurrence. Haildays are identified from operational radar-derived "Probability of Hail" (POH) data for two study regions, namely the north and south of the Swiss Alps. We use data from 2002–2022 during the convective season from April to September. The decision hailday YES / NO is based on surpassing a POH ≥ 80 % for a certain minimum footprint area of the domains. Separate logistic regression models and GAM´s are built for each domain and combined in an ensemble model to reconstruct the final time series. Overall, the models are able to describe the observed time series well. Historical hail reports are used for comparing years with the most and least haildays. For the northern and southern domains, the time series both show a significant positive trend in yearly aggregated haildays from 1959 to 2022. The trend is still positive and significant when looking at the period 1979–2022. In all models, the trends are driven by moisture and instability predictors. In the last two decades, we can see an increase in haildays at the beginning of the hail season and an earlier and longer peak, however, there is no systematic shift in the seasonal cycle. With this time series, we can now study the local and remote drivers of the interannual variability and seasonality of Swiss hail occurrence.