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

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 rob...

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Bibliographic Details
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
Description
Summary: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.

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