Predicting on the Extreme Precipitation in East and Southeast Asia during Summer 2022 from the Antecedent Soil Temperature Anomaly over the Tibetan Plateau

Abstract It has demonstrated that May land surface temperature (LST) and subsurface temperature (SUBT) anomalies on the Tibetan Plateau (TP) can serve as a predictor of June precipitation in downstream regions. In summer 2022, there was heavy precipitation (severe drought) in June (August) over southern China, coinciding with high May (July) LST/SUBT anomalies in TP. Under the framework of the Impact of Initialized Land Temperature and Snowpack on Subseasonal to Seasonal Prediction (LS4P), this study explores the causality between 2022 May (July) TP LST/SUBT and June (August) precipitation using an ensemble of land–atmosphere coupled model experiments. Results show that model prediction with imposed strongly positive May TP LST/SUBT (i.e., a triple) anomaly could produce 67% (46%) of June precipitation anomalies in South (northeast) China, while sea surface temperature (SST) effects account for 5% (51%). The atmosphere condensation heating and surface sensible heating due to TP LST/SUBT anomaly induce anomalous South Asian high, which moves eastward along with the Rossby waves. Meanwhile, the above process enhances upward motion, convective instability, and water vapor transport, eventually be favorable for rainfall formation in South China and northeast China. In Southeast Asia, it makes a downward movement, decrease in atmospheric water vapor, and unfavorable to the occurrence of precipitation. Further, July TP LST/SUBT contributes to the prediction skill for August record-breaking drought in southern China. Compared to SST effects, it is found that TP LST/SUBT effects significantly enhance the prediction skill for downstream region, suggesting that the TP land contributes an important source for subseasonal climate prediction. Significance Statement Land surface temperature (LST) and subsurface temperature (SUBT) are key elements of land processes and significantly affects local and remote atmosphere conditions in intraseasonal time scale. However, due to the lack of observation-based LST/SUBT dataset, previous studies have often used air temperature as a proxy to investigate the role of LST/SUBT in the land–atmosphere interaction and climate prediction. In this study, we conduct a set of land–atmosphere coupled model experiments to mainly explore the effects of the Tibetan Plateau (TP) LST/SUBT on the downstream summer precipitation predictability in 2022. Results reveal that May TP positive LST/SUBT anomaly contributes an important prediction skill to the extreme wet in June conditions in East Asia. The imposed TP LST/SUBT anomaly induces local atmosphere condensation heating and surface sensible heating, contributing to the dissipation of atmospheric perturbations, which transport eastward to the downstream regions along with the Rossby waves. The eastward transportation alters the vertical motion and water vapor. Consequently, positive precipitation anomaly occurs in South China and northeast China. Meanwhile, an opposite effect appears in Southeast Asia. Further, the July positive TP LST/SUBT anomaly puts added value in the extremely drought in August 2022. Overall, the anomalous TP LST/SUBT play important role in improving the predictive skill at the subseasonal scale.