Evaluation of Seasonal Precipitation in South Asian Monsoon Using FGOALS-f2 Seamless Prediction System

Abstract Timely and effective seasonal climate prediction is crucial to reduce the losses from hydrometeorological disasters by providing early warning. Evaluating the model’s performance is essential for its accurate interpretation and implementation. Here, we examined the precipitation forecast of the South Asian monsoon using the second-generation finite-volume version of the Flexible Global Ocean–Atmosphere–Land System Model (FGOALS-f2), a seamless prediction system. The hindcast skill was assessed using multiple metrics for lead times of 0–2 months. The result demonstrates that the prediction skill is positive across the majority of regions up to a 1-month lead. However, the skill varies on lead times and geographical areas, with notably better skills up to a 1-month lead in central (particularly surrounding Nepal) and western (southern Pakistan and Afghanistan) regions. Furthermore, we proposed a physical mechanism to explain the seasonal prediction skill of precipitation and the tropical sea surface temperature (SST) signal, along with the Indian Ocean dipole (IOD) relationship. Typically, the model shows better forecasting accuracy in regions where it accurately represents the strong connection with SST indices. Additionally, for a 0-month lead forecast, the model performs well in predicting El Niño–Southern Oscillation (ENSO) and IOD indices, with correlation values of 0.71 and 0.52, respectively. We also explored how tropical cyclones could affect predicting precipitation in South Asia. Finally, we presented and compared real-time forecasts from 2020 to 2023. The prediction results produced by FGOALS-f2 are conclusively valuable for most of South Asia.