Predictability and prediction of summer rainfall in the arid and semi-arid regions of China

Abstract

Northwest China (NWC) is an arid and semi-arid region where climate variability and environmental changes are sensitive to precipitation. The present study explores sources and limits of predictability of summer precipitation over NWC using the predictable mode analysis (PMA) of percentage of rainfall anomaly data. Two major modes of NWC summer rainfall variability are identified which are tied to Eurasian continental scale precipitation variations. The first mode features wet northern China corresponding to dry central Siberia and wet Mongolia, which is mainly driven by tropical Pacific sea surface temperature anomalies (SSTA). The second mode features wet western China reflecting wet Central Asia and dry Ural–western Siberia, which strongly links to Indian Ocean SSTA. Anomalous land warming over Eurasia also provides important precursors for the two modes. The cross-validated hindcast results demonstrate these modes can be predicted with significant correlation skills, suggesting that they may be considered as predictable modes. The domain averaged temporal correlation coefficient (TCC) skill during 1979 to 2015 using 0-month (1-month) lead models is 0.39 (0.35), which is considerably higher than dynamical models’ multi-model ensemble mean skill (−0.02). Maximum potential attainable prediction skills are also estimated and discussed. The result illustrates advantage of PMA in predicting rainfall over dry land areas and large room for dynamical model improvement. However, secular changes of predictors need to be detected continuously in order to make practical useful prediction.