On the Link between Summer Dry Bias over the U.S. Great Plains and Seasonal Temperature Prediction Skill in a Dynamical Forecast System

Soil moisture anomalies are expected to be a driver of summer predictability for the U.S. Great Plains since this region is prone to intense and year-to-year varying water and energy exchange between the land and the atmosphere. However, dynamical seasonal forecast systems struggle to deliver skillful summer temperature forecasts over that region, otherwise subject to a consistent warm-season dry bias in many climate models. This study proposes two techniques to mitigate the impact of this precipitation deficit on the modeled soil water content in a forecast system based on the CNRM-CM6-1 model. Both techniques lead to increased evapotranspiration during summer and reduced temperature and precipitation bias. However, only the technique based on a correction of the precipitation feeding the land surface throughout the forecast integration enables skillful summer prediction. Although this result cannot be generalized for other parts of the globe, it confirms the link between bias and skill over the U.S. Great Plains and pleads for continued efforts of the modeling community to tackle the summer bias affecting that region.