An Approach for Improving Short-Term Prediction of Summer Rainfall over North China by Decomposing Interannual and Decadal Variability

A statistical downscaling approach was developed to improve seasonal-to-interannual prediction of summer rainfall over North China by considering the effect of decadal variability based on observational datasets and dynamical model outputs. Both predictands and predictors were first decomposed into interannual and decadal components. Two predictive equations were then built separately for the two distinct timescales by using multivariate linear regressions based on independent sample validation. For the interannual timescale, 850-hPa meridional wind and 500-hPa geopotential heights from multiple dynamical models' hindcasts and SSTs from observational datasets were used to construct predictors. For the decadal timescale, two well-known basin-scale SST decadal oscillation (the Atlantic Multidecadal Oscillation and the Pacific Decadal Oscillation) indices were used as predictors. Then, the downscaled predictands were combined to represent the predicted/hindcasted total rainfall. The prediction was compared with the models' raw hindcasts and those from a similar approach but without timescale decomposition. In comparison to hindcasts from individual models or their multi-model ensemble mean, the skill of the present scheme was found to be significantly higher, with anomaly correlation coefficients increasing from nearly neutral to over 0.4 and with RMSE decreasing by up to 0.6 mm d(-1). The improvements were also seen in the station-based temporal correlation of the predictions with observed rainfall, with the coefficients ranging from -0.1 to 0.87, obviously higher than the models' raw hindcasted rainfall results. Thus, the present approach exhibits a great advantage and may be appropriate for use in operational predictions.