Evaluation of the sub-seasonal forecasting skill of SubX models for precipitation during recent multi-year droughts over the Korean Peninsula

Daily to monthly variations of precipitation directly affect the propagation of an emerging drought. To cope with adverse impacts, a skillful sub-seasonal forecast of precipitation is essential to track the evolution of the emerging drought and provide actionable information for stakeholder and water resources managers. This study evaluates the predictive performances of the Subseasonal Experiment (SubX) models (ECCC-GEPS6, EMC-GEFSv12, ESRL-FIMr1p1, GMAO-GEOS_V2p1, and RSMAS-CCSM4) for the precipitation variations during two recent long-term drought events (2007−2010 and 2013−2016) over the Korean Peninsula. Sub-seasonal prediction skill of SubX models are quantitatively evaluated via multiple verification metrics for ensemble, deterministic, and categorical forecasts. Results show that during the emergence of multi-year droughts, the intensification and persistence of drought severity are generally better predicted by SubX models than the weakening and recovery of the drought severity in all forecast times (1−4 weeks). The multi-model ensemble approach shows the best prediction skill, and EMC-GEFSv12 which has the most ensemble member presents the better predictive performance than other models. In addition, results from the sensitivity test to ensemble member size show that multiple ensemble member can enhance the prediction skills significantly up to eight ensemble members. Overall results suggest that the forecast of SubX on multi-year Korean Peninsula droughts can provide actionable information that helps manage water resources in a timely manner.