The main objective of the present study is to develop an efficient statistical downscaling (SD) approach for simulating simultaneously and concurrently daily precipitation series at many sites. The proposed approach consists of a combination of two distinct multiple regression models to represent the linkage between global climate predictors and and the probability of local daily rainfall occurrences and the daily rainfall amounts, and the singular value decomposition (SVD) technique to represent the observed statistical properties of the stochastic component of the proposed combined model. The feasibility of the suggested multisite downscaling method was assessed using observed daily precipitation data available at ten weather stations located in the southwest region of Quebec and southeast region of Ontario in Canada and the climate predictors estimated from the National Centre for Environmental Prediction (NCEP) re-analysis data set for the period from 1961 to 2000. It was found that the proposed SD approach was able to describe accurately various precipitation characteristics, including their spatial and temporal variations as well as their inter-annual anomalies. In particular, it has been shown that the proposed procedure was quite efficient in the simulation of daily precipitation series for many sites because of the effective computation of its SVD component.
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