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

• Published in: Geophysical Research Letters
• Main Authors: Gen Li, Lin Chen, Bo Lu
• Format: Article in Journal/Newspaper
• Language: English
• Published: Wiley 2023
• Subjects: July precipitation; central China; seasonal prediction; empirical model; ENSO; North Atlantic tripole SST mode; Geophysics. Cosmic physics; QC801-809; Pacific; North Atlantic
• Online Access: https://doi.org/10.1029/2022GL101463; https://doaj.org/article/f42aefc25320433cbe12dfa317c5a686

Abstract 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.