• winter precipitation;
  • statistical downscaling;
  • CFSv2;
  • SVD


Downscaling techniques can effectively improve the coarse resolution and poor representation of precipitation predicted by general circulation model (GCM). In this study, a statistical downscaling (SD) method, based on the singular value decomposition (SVD), is proposed for better representing the coupled variation between predictors and winter precipitation in China. By comparing current predictors from Climate Forecast System version 2 (CFSv2) of National Centers for Environmental Prediction and previous predictors from observation, the two best appropriate predictors, the winter sea level pressure (SLP) from the CFSv2 and the autumn sea-ice concentration (SIC) from observation, are selected to construct the SD model for prediction of winter precipitation in China. Three downscaling schemes are developed by involving the SLP, SIC, and both of them (i.e. SLP-scheme, SIC-scheme, and SS-scheme), respectively. Validations for the schemes show a considerable improvement of performance in predicting China winter precipitation, compared with the original CFSv2 output. The temporal and spatial anomaly correlation coefficient (ACC) and root mean square errors (RMSE) were estimated. For the cross validation, the spatial ACC are increased from ∼0.01 of the CFSv2 to >0.3 of the downscaling model. For the independent validation, the temporal RMSE from the downscaling schemes are all decreased more than 30%. In particular, the results using the SS-scheme showed relatively smaller RMSE than those of either the SLP-scheme or SIC-scheme, and hence can reproduce the precipitation anomaly in 2011 and 2012 winters more accurately.