Abstract: The middle and lower reaches of the Yangtze River are one of the regions with the most concentrated summer precipitation in China, and improving subseasonal prediction skills in this region is of great importance for flood prevention and disaster mitigation. Based on ensemble forecast data from three models (ECMWF, CMA, and UKMO) in the S2S project, this study systematically evaluates the forecasting skill and error structure of both single-model and multi-model ensemble systems for summer precipitation in the middle and lower Yangtze River region from lead pentad 1 to 6. It further quantitatively analyzes the model ability to forecast BSISO and its relationship with precipitation forecasting skill. The results indicate: (1) In deterministic forecasting, all models show that ensemble mean forecasts outperform control forecasts, with forecast skill gradually decreasing as lead time extends. ECMWF is the best overall, followed by UKMO, and CMA performs relatively weaker. The multi-model ensemble shows more stable and higher skill after the 3^rd lead pentad, demonstrating greater forecast extension potential. (2) Probability forecast evaluation shows that ECMWF performs the best across all precipitation thresholds, with forecast skill increasing at the beginning and then decreasing as the threshold rises. The multi-model ensemble significantly outperforms both the climate baseline and single-model forecasts across all lead times and thresholds. Brier score decomposition reveals that single-model forecasts for extreme high/low precipitation thresholds exhibit higher reliability but lower resolution compared to mid-range thresholds. In contrast, multi-model ensembles show significant improvements in both reliability and resolution. (3) The skill of models in forecasting BSISO signals is significantly positively correlated with their precipitation forecasting skill, showing correlation coefficients of greater than 0.9 and indicating that BSISO activity is a key source of subseasonal predictability for precipitation over this region. This study provides references for the application of models in subseasonal precipitation forecasting for the middle and lower Yangtze River, as well as for forecast calibration associated studies.