Statistical downscaling based on BP-CCA: Predictability and application to the winter temperature and precipitation in China.

The BP-CCA is used to build the optimal statistical downscaling model between the large- scale circulation over East Asia and the temperature/precipitation over China in boreal winter based on one year out cross validation. The predictability of temperature/precipitation in winter is analyzed. It shows that the average anomaly correlation coefficient (ACC) between the 500 hPa circulation and the temperature in winter is 0.7 with the highest ACC of 0.9. And the average ACC between the 500 hPa circulation and the precipitation in winter is 0.3 with the highest ACC being 0.7. Though the predictability of temperature/precipitation over China shows regional features, the 500 hPa potential heights still have close relationship with the temperature/precipitation in winter. The East Asia trough and western Pacific subtropical high are the most important systems which affect the temperature /precipitation in winter. Combining the BP-CCA method with the hindcast results of CGCM/NCC, we can obtain higher skill of forecasting temperature/precipitation via downscaling temperature/precipitation by means of the BP-CCA downs caling model than that in case only the CGCM/NCC model is used. The ability of the downscaling model comes from the high forecast skill of large scale circulations by the CGCM/NCC. 