A multi-site statistical downscaling model for daily precipitation using global scale GCM precipitation outputs

This study proposes a multi-site statistical downscaling model (MSDM), which can downscale daily precipitation series at multiple sites in a regional study area by utilizing Global Climate Models' (GCMs) precipitation outputs directly. The at-site precipitation occurrences and amount characteristics are reproduced by first-order Markov chain and probability mapping approaches, respectively. The spatial coherence of precipitation series among multiple sites is reproduced by adding correlated random noise series to GCM precipitation outputs. The model is applied for two regional study areas in southern Quebec (Canada). The MSDM results are compared to those of the local intensity scaling (LOCI) model, which is a single site downscaling model that uses GCM precipitation outputs. Both models reproduce probabilities of precipitation occurrence and mean wet-day precipitation amounts. However, the MSDM reproduces the observed precipitation occurrence Lag-1 autocorrelation, the standard deviation of the wet-day precipitation amounts, maximum 3-d precipitation total (R3days), and 90th percentile of the rain day amount (PREC90) better than the LOCI model. The MSDM also accurately reproduces cross-site correlations of precipitation occurrence and amount among multiple observation series.