The occurrence of summer extreme rainfall over southern China (SCER) is closely related with the boreal summer intraseasonal oscillation (BSISO). Whether the 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 play an important role in determining the forecast skill of SCER. The systematic assessment of reforecast data from the S2S database show that the ECMWF model performs a skillful prediction of BSISO1 index up to 24 days, while the skill of CMA model is about 10 days. Accordingly, the SCER occurrence is correctly predicted by ECMWF (CMA) model at a forecast lead time of ~14 (6) 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. Once the operational model well predicts the moisture convergence anomaly in the specific phases of BSISO1, the higher skill for the probability prediction of SCER is obtained. The present study implies that a further improvement in predicting the BSISO and its related moisture processes is crucial to facilitating the subseasonal prediction skill of SCER probability.