Abstract: Subseasonal-to-seasonal (S2S) precipitation prediction, characterized by its relatively long lead time, can provide reference information on a longer temporal scale. When applied to the headwater of the Yangtze River, it can effectively extend the timeliness of runoff prediction in the Yangtze River Basin. However, existing S2S precipitation prediction products often exhibit large errors and coarse spatial resolution in the headwater of the Yangtze River. When used to drive hydrological models that have high requirements for data quality, these products may introduce significant biases. Therefore, investigating how to reasonably utilize S2S precipitation prediction products for runoff prediction is of great research value. In this study, S2S prediction products from 4 global prediction centers were selected, and their precipitation prediction accuracy was evaluated. A daily-scale bias correction method was applied to the original precipitation prediction products. Furthermore, the study analyzed the impact of bias correction on the performance of S2S precipitation prediction, as well as the roles of two correction strategies (correction of driving data and correction of runoff) in S2S runoff prediction in the headwater of the Yangtze River. The results show that the S2S product from the European Centre for Medium-Range Weather Forecasts (ECMWF) performs the best in precipitation prediction. Bias correction can significantly improve the precipitation prediction performance of S2S products from different sources, which is specifically reflected in the reduction of errors between the predicted data (after bias correction) and the observed data. Moreover, the improvement effect becomes more pronounced as the lead time extends. Meanwhile, considering both the correction effect and computational procedures, direct runoff correction is more suitable for the bias correction of subseasonal-to-seasonal runoff prediction in the headwater of the Yangtze River. This study can provide technical references for the operational prediction of subseasonal-to-seasonal precipitation and runoff in the Yangtze River Basin.