How well does a convection-permitting climate model represent the reverse orographic effect of extreme hourly precipitation?

Abstract. 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 key to short-duration extremes. Recent studies reported a decrease in the intensity of extreme hourly precipitation with elevation. This “reverse orographic effect” could be related to processes which are sub-grid even for CPMs. 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 analysing extremes using conventional methods. We introduce the use of a non-asymptotic statistical approach (Simplified Metastatistical Extreme Value, SMEV) for the analysis of extremes from short time slices such as the ones of CPM simulations. We analyse an ERA-Interim-driven COSMO-crCLM 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 an 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 capability of estimating reliable rare extremes from short periods promises further application on short time-slice CPM projections, and model ensembles.