Statistical downscaling of precipitation on a spatially dependent network using a regional climate model

Abstract

We present a detailed downscaling simulation methodology for generating precipitation events, conditioned on external climate covariates, on a network of meteorological stations. These events can be input to hydrological models. To simulate an event on a future day t, the method uses the K-nearest neighbor algorithm to identify close neighbors from the historical record, and resamples a past event with resampling probabilities determined from external covariates. This preserves the spatial dependence of precipitation on the network, and other important distributional features of precipitation. Large numbers of arbitrary tuning parameters and model assumptions are reduced through the use of a multivariate Gaussian model relating climate covariates to historical precipitation. The approach is demonstrated by simulating daily precipitation, maximum temperature, and minimum temperature on a network of 93 locations in North Carolina, all conditioned on climate model output. The downscaling is based on a regional climate model (RCM) embedded within a global NCEP reanalysis model. The method is demonstrated using precipitation in North Carolina with the Canadian climate RCM as an NCEP driven RCM.