Abstract: How to improve the extended-range predictive skill is a hotspot and frontier research issue, which is crucial for bridging the gap in seamless prediction system. Based on the observations and reanalysis data during December 2005—August 2014, the Singular Value Decomposition analysis is used to reveal the highly coupled modes between the low-frequency precipitation over southern China and intraseasonal tropical convection/mid-latitude wave trains in boreal winter and summer, respectively. The BCC-CPS-S2Sv2 (hereafter referred to as BCC S2S) model provided by China Meteorological Administration is used to construct a set of dynamical-statistical models for subseasonal prediction of low-frequency precipitation anomalies over southern China using the statistical downscaling method. The BCC S2S model participates the Subseasonal-to-Seasonal Project and exhibits reasonable skills on the forecast of rainfall anomalies over most of southern China at 10—15 d forecast lead times during the independent prediction period of December 2014—August 2019. However, the dynamical-statistical model outperforms the BCC S2S model on precipitation forecast in terms of temporal variability over coastal region of South China (north of the Yangtze river) during winter (summer) and the spatial distribution and extreme events beyond 15—20 d forecast lead. The idea and method proposed by this study can be widely applied to extended-range prediction of other regional meteorological elements and extreme events.