Factors determining the subseasonal prediction skill of summer extreme rainfall over southern China

Abstract

The occurrence of summer extreme rainfall over southern China (SCER) is closely related to the boreal summer intraseasonal oscillation (BSISO), and whether operational models can reasonably predict the BSISO evolution and its modulation on SCER probability is crucial for disaster prevention and mitigation. Here, we find that the skill of subseasonal-to-seasonal (S2S) operational models in predicting the first component of BSISO (BSISO1) might determine the forecast skill of SCER. A systematic assessment is conducted on the reforecast data from two operational models that participated in the S2S project, i.e., the model of European Centre for Medium-Range Weather Forecasts (ECMWF) and the model of China Meteorological Administration (CMA). The results show that the ECMWF model can yield skillful prediction of the BSISO1 index up to 24 days in advance, while the skill of the CMA model is about 10 days. Accordingly, the SCER occurrence is correctly predicted by ECMWF (CMA) model at a forecast lead time of ~ 14 (7) days. The diagnostic results of modeled moisture processes further suggest that the anomalous moisture convergence (advection) induced by the BSISO1 activity serves as the primary (secondary) source of subseasonal predictability of SCER. With better prediction of the moisture convergence anomaly in the specific phases of BSISO1, higher skills can be obtained in the probability prediction of SCER. The present study implies that a further improvement in predicting the BSISO and its related moisture processes is crucial to promoting the subseasonal prediction skill of SCER probability.