Author(s): Myeong-Ho Yeo; Van-Thanh-Van Nguyen
Linked Author(s): Van-Thanh-Van Nguyen
Keywords: Daily rainfalls; Statistical modeling; Downscaling methods; Global climate models; Climate change impacts
Abstract: The present study proposes a statistical downscaling (SD) model for describing accurately the linkage between large-scale climate predictors and observed daily rainfall characteristics at a local site. The proposed model was based on a combination of a logistic regression model for representing the daily rainfall occurrences and a nonlinear regression model for describing the daily precipitation amounts. The feasibility of the suggested SD method was tested using the NCEP re-analysis data and the observed daily precipitation data available for the 1961-2001 period at two study sites located in completely two different climatic regions: the Seoul station in tropical-climate Korea and the Dorval Airport station in cold-climate Canada. It was found that it is feasible to link large-scale climate predictors given by GCM simulation outputs with daily precipitation characteristics at both stations. Furthermore, the proposed SD method could provide more accurate results than those given by the currently popular SDSM method.