Estimating future short-duration extreme precipitation in mountainous regions is fundamental for risk management. High-resolution convection-permitting models (CPMs) represent the state of the art for these projections, as they resolve convective processes that are key to short-duration extremes. Recent observational studies reported a decrease in the intensity of extreme hourly precipitation with elevation. This “reverse orographic effect” could be related to processes which are subgrid even for CPMs. To quantify the reliability of future projections of extreme short-duration precipitation in mountainous regions, it is thus crucial to understand to what extent CPMs can reproduce this effect. Due to the computational demands however, CPM simulations are still too short for analyzing extremes using conventional methods. We use a non-asymptotic statistical approach (Simplified Metastatistical Extreme Value: SMEV) for the analysis of extremes from short time periods, such as the ones of CPM simulations. We analyze an ERA-Interim-driven Consortium for Small-Scale Modeling (COSMO-crCLIM, convection-resolving Climate Modelling) simulation (2000–2009; 2.2 km resolution), and we use hourly precipitation from 174 rain gauges in an orographically complex area in northeastern Italy as a benchmark. We investigate the ability of the model to simulate the orographic effect on short-duration precipitation extremes, as compared to observational data. We focus on extremes as high as the 20-year return levels. While overall good agreement is reported at daily and hourly duration, the CPM tends to increasingly overestimate hourly extremes with increasing elevation, implying that the reverse orographic effect is not fully captured. These findings suggest that CPM bias-correction approaches should account for orography. SMEV's capability of estimating reliable rare extremes from short periods promises further applications on short-time-period CPM projections and model ensembles.

Reliable projections of extreme future precipitation are fundamental for risk management and adaptation strategies. Convection‐permitting models (CPMs) explicitly resolve large convective systems and represent sub‐daily extremes more realistically than coarser resolution models, but present short records due to the high computational costs. Here, we evaluate the potential of a non‐asymptotic approach, the Simplified Metastatistical Extreme Value (SMEV) to provide information on the future change of extreme sub‐daily return levels based on CPM simulations. We focus on a complex‐orography area in the North Eastern Italy and use three 10‐year time periods COSMO‐crCLIM simulations (2.2 km resolution) under RCP8.5 scenario. When compared to a block r‐maxima approach currently used in similar applications, the proposed approach shows reduced uncertainty in rare return level estimates (about 5%–10% smaller confidence interval) and can improve the quantification of future changes from CPM simulations. We evaluate these changes and their statistical significance in return levels for 1–24 hr durations. The changes show an interesting spatial organization associated with orography, with significant positive changes located at high elevations. These positive changes tend to increase with increasing return period and decreasing duration. Because SMEV can separate the roles of event intensity and occurrence, it allows for physical interpretations of these changes. We suggest that non‐asymptotic approaches permit the quantification of change in rare extremes within available CPM runs.

Quantifying the future probability of sub‐daily extreme precipitation in a changing climate is crucial for risk management, engineering, and insurance. Kilometer‐scale convection‐permitting climate models (CPMs) represent convective precipitation and complex terrain more realistically than other climate models, thereby enhancing the representation of sub‐daily extremes. This study employs a novel statistical approach to evaluate projected changes in extreme sub‐daily precipitation and provides a physical interpretation of their driving processes. It focuses on the complex‐topography area of northern Italy, where resides almost half of the Italian population and a significant portion of the Italian economy, with a rich diversity in industry, agriculture, tourism. We use precipitation data from a CPMs ensemble covering three periods: historical (1996–2005), near future (2041–2050), far future (2090–2099) under the RCP8.5 scenario. Sub‐daily to daily precipitation extremes with return periods up to 100 years are examined. We find a general intensification of extremes across all durations (from 1 to 24 hr), stronger at shorter durations and rarer probabilities. Spatial patterns vary with duration, with higher and significant increases emerging in mountainous areas in Eastern Alps and North Apennines. The detected changes cannot be explained by thermodynamics alone, highlighting the modulating role of the changes in atmospheric dynamics. These findings are crucial for enhancing risk management strategies and adapting to natural hazards in a warming climate. This approach may be exploited in larger scale analysis.