Error Variations in the Sub-Seasonal Precipitation Prediction of the BCC-CPS-S2Sv2 in Summer over China

The application of sub-seasonal prediction error analysis and interpretation remains a key research frontier of climate prediction. Related studies are essential for deepening the understanding of model performance, investigating error sources, and improving model prediction accuracy. Based on four perspectives—basic characteristics, spatial and temporal consistency, and potential causes of prediction errors—this study systematically investigates the sub-seasonal hindcast errors of summer precipitation over China from the state-of-the-art model system BCC–CPS–S2Sv2 (S2Sv2). The findings reveal that (1) S2Sv2 shows significant prediction skill in the first three pentads and shows an obvious increase in RMSE and a decrease in SCC of the precipitation prediction. This feature is quite similar to other operational models. (2) The prediction error primarily presents typical modes such as the meridional dipole, triple or consistent patterns. Prediction errors maintain spatial consistency in South China, the middle and lower reaches of the Yangtze River, and North China. Temporal consistency of the prediction errors shows a significant correlation between neighboring pentads, which diminishes as the prediction period extends, with a better correlation lasting for 2–4 pentads in some areas. (3) The prediction errors of S2Sv2 are significantly correlated with major circulation patterns in East Asia and key area sea surface temperatures (SSTs). For instance, errors are larger when the West Pacific Subtropical High is stronger, but smaller when the East Asia Trough is stronger. Errors also decrease with higher SSTs in the central equatorial Pacific and increase with higher SSTs in the tropical North Atlantic. This study provides valuable insights into the limitations of the S2Sv2 model and offers important references for error correction and model application.