Comparative Evaluation of Short-Range Extreme Rainfall Forecast by Two High-Resolution Global Models

Accurate prediction of extreme rainfall events during the Indian Summer Monsoon (ISM, June to September) is critical for disaster preparedness and mitigation. This study evaluates the performance of two operational numerical weather prediction models, a high-resolution version of Global Forecast System (GFS T1534) and the control member of the Met Office Global and Regional Ensemble Prediction System-Global (MOGREPS-G), in forecasting such events during the ISM from 2020 to 2023. The results demonstrate that, with respect to observations, both models tend to underestimate the mean and variability of rainfall; GFS-T1534 represents the mean and correlation better while MOGREPS-G represents the variability better over the Indian landmass. To assess the models’ performance for extreme rainfall prediction, we fix a rainfall threshold of 50 mm day−1, and the skill scores are computed including Probability of Detection, False Alarm Rate, Bias score and F1 score. Together, these scores indicate that both models show potential in short-range forecasting of extreme rainfall events, particularly within 24 h, but their skills remain limited at longer lead times. Specifically, the model biases vary over different geographical locations, often showing contrasting features. This underscores the need for model-specific post-processing and calibration techniques if these forecasts are to be used effectively for operational decision-making.