Bias Correction to Improve the Skill of Summer Precipitation Forecasts as Produced by North American Multi-Model Ensemble System over Contiguous United States

Improvements in the skill of precipitation forecast in summer as produced by North American Multi-Model Ensemble (NMME) system over Contiguous United States (CONUS) are examined by applying a new bias correction method. The uncorrected precipitation produced by NMME hindcasts exhibits good prediction skill in fall and winter while the spring and summer forecasts are marked with extremely poor skill. The correction method deployed in this study decreases the forecasted precipitation distribution error by utilizing skillfully predicted 2-m air temperature (T2m) forecast. This method averts the low skills of precipitation forecast in summer by exploiting the strong co-variability that exists between precipitation and T2m in nature, and take advantage of the enhanced recycled precipitation occurrence over CONUS in summer to provide an ideal situation to horn the precipitation forecast skills using the T2m forecasts. The proposed bias correction is shown to successfully reduce the root mean square error in precipitation hindcasts in summer and can easily be extended to real-time forecasts as well as providing a framework to dynamically link precipitation with other predictors besides T2m.