Why Forecasts Expected Dry Conditions amid South China’s Record-Breaking Rainfall in Early Summer 2022?

In May–June 2022, South China (SC) experienced record-breaking rainfall, resulting in severe flooding and significant socioeconomic impacts, which posed a challenge to operational seasonal forecasting efforts. Notably, most forecast systems and their multi-model ensemble severely underestimated the SC floods in early summer 2022, forecasting decreased rainfall instead. Observational analysis links the 2022 SC floods to an anomalous low-level anticyclone over the western North Pacific and a zonal wave train across the mid-to-high latitudes of Eurasia. The forecast systems, however, generally missed the Eurasian zonal wave train and predicted an anomalous low-level cyclone over the western North Pacific, which would typically result in decreased rainfall over SC. Further analysis suggests that the La Niña-related cold sea surface temperature (SST) anomalies in the tropical Pacific, accurately predicted by the forecast systems, contributed to the modeled anomalous cyclone over the western North Pacific. Excessive reliance on the La Niña-related Pacific SST anomalies appears to be the primary driver of the inaccurate prediction of the 2022 SC floods. The SST bias in the North Atlantic also contributed, albeit to a lesser extent, by influencing the anomalous western North Pacific anticyclone and the Eurasian mid-to-high latitude wave train. Additionally, certain ensemble members from the forecast systems did predict increased rainfall over SC in their forecasts, yet exhibited a spurious precipitation mechanism inconsistent with observations, primarily due to distinguished differences in the atmospheric circulation patterns over SC and Eurasian mid-to-high latitudes. This implies that factors beyond the Pacific La Niña might have played a more significant role in sustaining the 2022 SC floods. The study emphasizes the need to improve seasonal forecast systems, particularly in their representation of atmospheric internal variability and external forcing beyond the Pacific La Niña/El Niño.