Dynamic–statistic combined ensemble prediction and impact factors of China's summer precipitation

Abstract. ​​​​​​​The dynamic–statistic prediction shows excellent performance with regard to monthly and seasonal precipitation prediction in China and has been applied to several dynamical models. In order to further improve the prediction skill of summer precipitation in China, the unequal-weighted ensemble prediction (UWE) using outputs of the dynamic–statistic prediction is presented, and its possible impact factors are also analysed. Results indicate that the UWE has shown promise in improving the prediction skill of summer precipitation in China on account of the fact that the UWE can overcome shortcomings with regard to the structural inadequacy of individual dynamic–statistic predictions, reducing formulation uncertainties and resulting in more stable and accurate predictions. Impact factor analysis indicates that (1) the station-based ensemble prediction, with an anomaly correlation coefficient (ACC) of 0.10–0.11 and a prediction score (PS) score of 69.3–70.2, has shown better skills than the grid-based one as the former produces a probability density distribution of precipitation that is closer to observations than the latter. (2) The use of the spatial average removed anomaly correlation coefficient (SACC) may lower the prediction skill and introduce obvious errors into the estimation of the spatial consistency of prediction anomalies. SACC could be replaced by the revised anomaly correlation coefficient (RACC), which is calculated directly using the precipitation anomalies of each station without subtracting the average precipitation anomaly of all stations. (3) The low dispersal intensity among ensemble samples of the UWE implies that the historically similar errors selected by means of different approaches are quite close to each other, making the correction of the model prediction more reliable. Therefore, the UWE is expected to further improve the accuracy of summer precipitation prediction in China by considering impact factors such as the grid- or station-based ensemble approach, the method of calculating the ACC, and the dispersal intensity of ensemble samples in the application and analysis process of the UWE.