Statistical Downscaling of Precipitation in Korea Using Multimodel Output Variables as Predictors

A pattern projection downscaling method is applied to predict summer precipitation at 60 stations over Korea. The predictors are multiple variables from the output of six operational dynamical models. The hindcast datasets span a period of 21 yr from 1983 to 2003. A downscaled prediction was made for each model separately within a leave-one-out cross-validation framework. The pattern projection method uses a moving window, which scans globally, in order to seek the most optimal predictor for each station. The final forecast is the average of six model downscaled precipitation forecasts using the best predictors and will be referred to as "DMME." It is found that DMME significantly improves the prediction skill by correcting the erroneous signs of the rainfall anomalies in coarse-resolution predictions of general circulation models. Although Korea's precipitation is strongly influenced by local mountainous terrain, DMME performs well at 59 stations with correlation skill significant at the 95% confidence level. The improvement of the prediction skill is attributed to three steps: coupled pattern selection, optimal predictor selection, and the multimodel downscaled precipitation ensemble. This study indicates that the large-scale circulation variables, which are predicted by the current operational dynamical models, if selected, can be used to make skillful predictions of the local precipitation by using appropriate statistical downscaling methods.