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A linear regression model is constructed to predict the April–May precipitation in central China (PCC) with a lead time of 1–2 months. This model not only reproduces the historical April–May PCC from 1985 to 2006 but also demonstrates strong robustness and reliability during the independent test period of 2007–16. Two preceding factors are selected to build the model, the February–March Arctic stratospheric ozone (ASO) and Indian Ocean sea surface temperature (IO SST ), indicating a synergistic impact of Arctic and tropical signals on the midlatitude climate. A possible mechanism of ASO changes affecting Chinese precipitation is that the stratospheric circulation anomalies related to ASO changes may downward influence circulation over North Pacific, and then extend westward to influence East Asia, leading to changes in Chinese precipitation. Anomalies of the other predictor, IO SST , are associated with a baroclinic structure over central China. For example, warm IO SST  causes anomalous convection over central China and affects the warm and humid airstream flowing from the Pacific to China. These processes related to the two predictors result in the April–May PCC anomalies. Sensitivity experiments and a transient experiment covering a longer period than the observations/reanalysis support the results from our statistical analysis based on observations. It implies that this statistical model could be applied to the output of seasonal forecasts from observations and improve the forecasting ability of April–May PCC in the future.

Using Observed Signals from the Arctic Stratosphere and Indian Ocean to Predict April–May Precipitation in Central China