Future changes in sub-daily extreme precipitation over a complex-orography area from a convection-permitting climate model

Abstract

Sub-daily extreme precipitation can generate fast hydro-geomorphic hazards such as flash floods and debris flows, which cause fatalities and damages especially in mountainous regions. Reliable projections of extreme future precipitation is fundamental for risk management and adaptation strategies. Convection-permitting climate models (CPMs) esplicitely resolve large convective systems and represent local processes, especially sub-daily extreme precipitation, more realistically than coarser resolution models, thus leading to higher confidence in their projections. Given their high computation cost, however, the available CPM simulations cover relatively short time periods (10–20 years), too short for deriving precipitation frequency analyses with conventional extreme value methods based on annual maxima or threshold exceedances.In this work, we evaluate the potential of a non-asymptotic approach based on “ordinary” events, the so-called Simplified Metastatistical Extreme Value (SMEV), to provide information on the future change of short-duration precipitation extremes. We focus on a complex-orography region in the Eastern Italian Alps, where significant changes in sub-daily annual maxima have been already observed. The study is based on COSMO-crCLIM model simulations at 2.2 km resolution under the RCP8.5 scenario and uses three 10-year time periods: historical 1996-2005 (the control period), near-future 2041-2050 and far future 2090-2099. We estimate extreme precipitation for durations ranging from 1 h to 24 h and assess the projected changes with respect to the control period. Specifically, we analyze annual maxima, return levels up to 50 years, and the parameters of the statistical model. A bootstrap procedure is used to evaluate the uncertainty of the estimates, and a permutation test is applied to assess the statistical significance of the projected changes. We compare our results with a modified Generalized Extreme Value (GEV) approach, recently applied for the study of extremes in CPM future time periods.We found that annual maxima and higher return levels exhibit a general increase in the future especially for the far future and the shorter event durations. On average, the magnitude of the far future change decreases with the precipitation temporal scale. The changes show an interesting spatial organization that can be associated with the orography of the region: significant future increases are mostly located at high elevations, while lowlands and coastal zones show no clear pattern.This work shows that SMEV reduces the uncertainty in the estimates of higher return levels compared to GEV and can thus provide improved estimates of their future changes from short CPM runs. These findings advance our knowledge about the projected changes in extreme precipitation and their spatial distribution at the different time scales. They can thus help improving risk management and adaptation strategies.