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Regional climate models (RCMs) are valuable tools to evaluate impacts ofclimate change (CC) at regional scale. However, as the size of the area ofinterest decreases, the ability of a RCM to simulate extreme precipitationevents decreases due to the spatial resolution. Thus, it is difficult toevaluate whether a RCM bias on localized extreme precipitation is caused bythe spatial resolution or by a misrepresentation of the physical processesin the model. Thereby, it is difficult to trust the CC impact projectionsfor localized extreme precipitation. Stochastic spatial disaggregationmodels can bring the RCM precipitation data at a finer scale and reduce thebias caused by spatial resolution. In addition, disaggregation models cangenerate an ensemble of outputs, producing an interval of possible valuesinstead of a unique discrete value.  The objective of this work is to evaluate whether a stochastic spatialdisaggregation model applied on annual maximum daily precipitation (i)enables the validation of a RCM for a period of reference, and (ii) modifiesthe evaluation of CC impacts over a small area. Three simulations of theCanadian RCM (CRCM) covering the period 1961–2099 are used over a smallwatershed (130 km<sup>2</sup>) located in southern Québec, Canada. Thedisaggregation model applied is based on Gibbs sampling and accounts forphysical properties of the event (wind speed, wind direction, and convectiveavailable potential energy – CAPE), leading to realistic spatialdistributions of precipitation. The results indicate that disaggregation hasa significant impact on the validation. However, it does not provide aprecise estimate of the simulation bias because of the difference inresolution between disaggregated values (4 km) and observations, and becauseof the underestimation of the spatial variability by the disaggregationmodel for the most convective events. Nevertheless, disaggregationillustrates that the simulations used mostly overestimated annual maximumprecipitation depth in the study area during the reference period. Also,disaggregation slightly increases the signal of CC compared to the RCM rawsimulations, highlighting the importance of spatial resolution in CC impactevaluation of extreme events

hydrology and earth system sciences

Stochastic spatial disaggregation of extreme precipitation to validate a regional climate model and to evaluate climate change impacts over a small watershed