Stochastic Rainfall Downscaling of Climate Models

Abstract

Abstract Precipitation extremes and small-scale variability are essential drivers in many climate change impact studies. However, the spatial resolution currently achieved by global climate models (GCMs) and regional climate models (RCMs) is still insufficient to correctly identify the fine structure of precipitation intensity fields. In the absence of a proper physically based representation, this scale gap can be at least temporarily bridged by adopting a stochastic rainfall downscaling technique. In this work, a precipitation downscaling chain is introduced where the global 40-yr ECMWF Re-Analysis (ERA-40) (at about 120-km resolution) is dynamically downscaled using the Protheus RCM at 30-km resolution. The RCM precipitation is then further downscaled using a stochastic downscaling technique, the Rainfall Filtered Autoregressive Model (RainFARM), which has been extended for application to long climate simulations. The application of the stochastic downscaling technique directly to the larger-scale reanalysis field at about 120-km resolution is also discussed. To assess the ability of this approach in reproducing the main statistical properties of precipitation, the downscaled model results are compared with the precipitation data provided by a dense network of 122 rain gauges in northwestern Italy, in the time period from 1958 to 2001. The high-resolution precipitation fields obtained by stochastically downscaling the RCM outputs reproduce well the seasonality and amplitude distribution of the observed precipitation during most of the year, including extreme events and variance. In addition, the RainFARM outputs compare more favorably to observations when the procedure is applied to the RCM output rather than to the global reanalyses, highlighting the added value of reaching high enough resolution with a dynamical model.