A singular-value-decomposition (SVD) statistical downscaling technique was developed for monthly rainfall over southern Taiwan. The statistical model was applied to seven different general circulation models. Seven different geographical domains for the large-scale atmospheric predictors were tested and their effects on rainfall projections were evaluated. Because different climate models indicate different future rainfall projections, a multi-model ensemble approach was applied to provide best guess estimates. Using the multi-model ensemble, and a range of metrics, it was found that the different predictor geographical domains had little influence on the projected monthly rainfalls. Two emission climate change scenarios (A1B and B1) were used to project the future rainfalls for the period from 2010 to 2045 across southern Taiwan. Overall, future rainfall shows an increasing trend during the May-to-October wet season and a decreasing trend during the November-to-April dry season.
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