Precipitation forecasts, particularly at subseasonal-to-seasonal (S2S) time scale, are essential for informed and proactive water resources management. Although S2S precipitation forecasts have been evaluated, no systematic decomposition of the skill, Nash-Sutcliffe Efficiency (NSE) coefficient, has been analyzed towards understanding the forecast accuracy. We decompose the NSE of S2S precipitation forecast into its three components – correlation, conditional bias, and unconditional bias – by four seasons, three lead times (1–12-day, 1-22 day, and 1-32 day), and three models (ECMWF, CFS, NCEP) over the Conterminous United States (CONUS). Application of dry mask is critical as the NSE and correlation are lower across all seasons after masking areas with low precipitation values. Further, a west-to-east gradient in S2S forecast skill exists and forecast skill was better during the winter months and for areas closer to the coast. Overall, ECMWF’s model performance was stronger than both ECCC and NCEP CFS’s performance, mainly for the forecasts issued during fall and winter months. However, ECCC and NCEP CFS performed better for the forecast issued during the spring months, and also performed better in in-land areas. Post-processing using simple Model Output Statistics could reduce both unconditional and conditional bias to zero, thereby offering better skill for regimes with high correlation. Our decomposition results also show efforts should focus on improving model parametrization and initialization schemes for climate regimes with low correlation values.