A Physics‐Based Empirical Model for the Seasonal Prediction of the Central China July Precipitation.

July is the rainy peak month of central China, with a large interannual variation of local precipitation often causing serious droughts and floods. The seasonal prediction of the central China July precipitation (CCJP) is an important but still challenging task. Here, we suggest several robust seasonal predictors for the CCJP, including the preceding winter intensity of El Niño‐South Oscillation (ENSO), the winter‐to‐spring decaying rate of ENSO signals in the central Pacific, as well as the spring tropical and subpolar North Atlantic sea surface temperature anomalies. A physics‐based empirical model is then developed to predict the CCJP by using the principal component regression of the aforementioned seasonal predictors. In our statistical model, the seasonal prediction skill of the CCJP is high, with the cross‐validated reforecast skill at 0.81 during 1993–2021. This suggests a skillful seasonal prediction of the CCJP, with potentially enormous benefits for the local society and economy. Plain Language Summary: July contributes about 20% of annual precipitation for the densely populated central China, which could exert tremendous socio‐economic impacts over the region, including agriculture, water resources, food security, ecosystems, disaster mitigation, infrastructure construction, human health, and so on. Thus, how to skillfully predict the interannual variation of the central China July precipitation (CCJP) is a widespread scientific and socio‐economic concern. This work establishes a statistical model for the seasonal prediction of the CCJP by combining the physical precursor factors that drive the interannual variation of the CCJP. Our physics‐based empirical model can well predict the CCJP at one‐season lead, with the cross‐validated reforecast skill at 0.81 during 1993–2021. This provides a substantial skill of the seasonal prediction of the CCJP and is of potentially great importance to the regional agrarian‐based livelihood of hundreds of millions of people. Key Points: The Pacific and North Atlantic sea surface temperature precursors are suggested as robust seasonal predictors of the central China July precipitation (CCJP)A physics‐based empirical prediction model of the CCJP is developed based on the principal component regression of the seasonal predictorsThe prediction skill of the CCJP in our statistical model is high, with a cross‐validated reforecast skill at 0.81 during 1993–2021 [ABSTRACT FROM AUTHOR]