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Abstract

Year-to-year variations of southern Thailand rainfall (STR) in boreal winter exert profound social and economic impacts, whereas current multimodel ensemble prediction systems have low skills to predict the STR. This study proposes a physical-based seasonal prediction model for the winter STR 1 month in advance using the outputs from the dynamic models. The prediction model is constructed using linear regression, with the tropical western Pacific (TWP) sea surface temperature (SST) anomaly in preceding October as a predictor. Its prediction skill in the leave-five-out cross-validation is significantly higher than that of the multimodel ensemble mean. The mechanism behind this model is also discussed. In October, the warm TWP SST anomalies can trigger anomalous low-level convergence surrounding the South China Sea in terms of the Matsuno-Gill mechanism and persist into the following winter, causing above-than-normal STR. This information is essential and may provide another perspective to improve the model prediction on the winter STR.