A hybrid statistical-dynamical prediction scheme for summer monthly precipitation over Northeast China

To improve the seasonal prediction of monthly precipitation in summer over Northeast China (NEC), a hybrid prediction scheme is developed to combine the advantages of statistical method with dynamical prediction information from 4 coupled general climate models (CGCMs). As the operational prediction of summer climate is performed in March or earlier, the information of CGCMs employed in this study is from the hindcast/prediction in February for June and July, and March for August. Predictors comprise observations of the preceding winter sea surface temperature and simultaneous information derived from the CGCMs. Stability and multicollinearity are fully considered in predictor selection to avoid over-fitting. For a single model, the monthly precipitation reconstructed from the predicted time series and the observed spatial load of the leading Empirical Orthogonal Function (EOF) modes with accumulative explained covariance more than 85%. For the multi-model ensemble (MME), the predictions with the lowest correlation coefficient were removed until the performance of the MME was optimal for the cross-validation period. This new ensemble method shows some advantages compared to a traditional MME method. The leave-one-out cross-validation for 1982-2010 and the independent validation for 2011-2016 both indicate an improvement of the new hybrid scheme in seasonal predictions of summer monthly precipitation over NEC. The observed and predicted monthly precipitation are significantly correlated with coefficients of 0.71, 0.49, and 0.73, and their hit rates are 77%, 66%, and 77% for June, July, and August precipitation, respectively for the MME over the period 1982-2016.