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 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 over the Indian region during the JJAS seasons from 2020 to 2023. Results show that both models tend to underestimate the mean and variability of rainfall, with GFS T1534 represents the mean and correlation better while MOGREPS-G represents the variability better over the Indian Landmass. Further a statistics based extreme rainfall threshold (50 mm/day) is fixed to compute the skill scores including Probability of Detection (POD), False Alarm Rate (FAR), and Bias. GFS T1534 shows moderate POD values but is limited by high FAR and underestimation biases. In contrast, MOGREPS-G displays higher POD at shorter lead times but suffers from elevated FAR and a strong over-forecasting tendency, particularly beyond 24 hours. The findings highlight strengths and limitations of both models in context of their operational use in extreme rainfall forecasting and early warning systems in India.