Improving the seasonal forecast for summertime South China rainfall using statistical downscaling

The performance of various seasonal forecast systems in predicting the station-scale summer rainfall in South China (SC) was assessed and was compared with that based on a statistical downscaling scheme. Hindcast experiments from 11 dynamical models covering the period of 1983-2003 were taken from the Asia-Pacific Economic Cooperation Climate Center multimodel ensemble. Based on observations, singular value decomposition analysis (SVDA) showed that SC precipitation is strongly related to the broad-scale sea level pressure (SLP) variation over Southeast Asia, western north Pacific, and part of the Indian Ocean. Analogous covariability was also found between model hindcasts and the observed station precipitation. Based on these results from SVDA, a statistical downscaling scheme for predicting SC station rainfall with model SLP as predictor was constructed. In general, the statistical scheme is superior to the original model prediction in two geographical regions, namely, western SC (near Guangxi) and eastern coastal SC (eastern Guangdong to part of Fujian). Further analysis indicated that dynamical models are able to reproduce the large-scale circulation patterns associated with the recurrent modes of SC rainfall, but not the local circulation features. This probably leads to erroneous rainfall predictions in some locations. On the other hand, the statistical scheme was able to map the broad-scale SLP patterns onto the station-scale rainfall anomalies, thereby correcting some of the model biases. Overall, our results demonstrate how SC summer rainfall predictions can be improved by tapping the source of predictability related to large-scale circulation signals from dynamical models.