A stochastic approach for the continuous simulation of ordinary and extreme precipitation in Alpine environments

Predicting the impacts of climate change on hydroclimatic processes in small mountainous catchments requires long and realistic high-temporal-resolution simulations of key environmental variables, particularly precipitation, under future scenarios. Stochastic models provide an effective way to generate multi-decadal projections, but existing approaches struggle to reproduce the alternation of weather systems and sub-hourly extremes. We propose a stochastic framework that accurately describes both ordinary and extreme precipitation events, explicitly links intermittency with event inter-arrival characteristics, and represents different storm types (e.g., convective and stratiform). Our approach combines CoSMoS, which generates stochastic time series preserving probability distributions and correlation structures, with concepts from TENAX, which relates the occurrence frequency and the probability distribution of extreme precipitation to near-surface temperature. Climate change impacts are incorporated through projected changes in temperature distributions and large-scale weather patterns from regional climate models. The method is tested on the Rio Valfredda, a small Alpine catchment in the eastern Italian Alps. The sub-hourly resolution of the framework allows explicit representation of convective precipitation, a key driver of extreme events in Alpine environments.