Sub-seasonal to Seasonal(S2S) hydrological prediction is a core supporting technology for watershed water resource scheduling and flood control & disaster reduction. A semi-distributed hydrological modeling system for the Huaihe River Basin was established based on the SWAT2012 model. Runoff prediction was carried out by driving the SWAT model with historical hindcast data from three sub-seasonal to seasonal (S2S) climate models (CMA-CPSv3, CMME-S2S, and AH-EDDS) from 2006 to 2024 (AH-EDDS from 2009 to 2024). The simulation accuracy of the hydrological model and the prediction skills of the climate models were systematically evaluated. The results show that: the calibrated SWAT model exhibited excellent simulation accuracy during both the calibration period (1981-2010) and validation period (2011-2020), with a coefficient of determination (R2)≥0.88 and Nash-Sutcliffe efficiency (NSE)≥0.85. Four parameters, including groundwater delay time, are the most sensitive parameters. Runoff prediction skills driven by S2S models show significant seasonal differences: the effective lead time in the dry season can reach 40 days, while the effective lead time of CMA-CPSv3 and CMME-S2S is only 1–5 days and that of AH-EDDS is 6–10 days in the wet season. The overall prediction skill in the dry season is higher than that in the wet season. All three models show low hit rate, high false alarm rate and systematic underestimation for extreme precipitation, among which AH-EDDS performs the best in spatial precipitation prediction and comprehensive runoff prediction. The tercile probabilistic prediction and extreme event threshold method can effectively capture watershed hydrological risks, successfully capturing the flood signals in the wet seasons of 2008, 2016, 2020 and the drought signals in the dry seasons of 2009, 2011, 2014. The framework of “multi-model S2S climate prediction driving hydrological model” established in this study can effectively improve the reliability of sub-seasonal to seasonal runoff prediction in the Huaihe River Basin, which has been verified in real time during the 2025 wet season and shows potential for operational application.