Assessment of the GraphCast AI model for precipitation forecasting and its potential in extreme event prediction over Bangladesh

Munad Hasan , Shabista Yildiz , Mohammad Kamruzzaman

Bangladesh situated in the tropical monsoon region is one of the most rainfall-sensitive countries in the world with terrain ranging from northwest floodplains to southern coastal deltas to eastern hilly regions. This complex landscape coupled with intensified climate variability influences local convection and extreme precipitation events, making short range forecasting particularly difficult. In this context, AI driven weather forecasting is gaining promise in diagnosing nonlinear atmospheric processes where conventional physics-based models fall short. Therefore, this study employs AI-based GraphCast model to forecast 1-, 2-, and 3-day cumulative rainfall over Bangladesh utilizing observational data from 43 Bangladesh Meteorological Department (BMD) stations during 2023-2024. Then, the performance of the model has been evaluated against global forecasting models namely ECMWF and GFS with statistical metrics including correlation coefficient (CC), mean error (ME), root mean square error (RMSE), and probability of detection (POD). The capability of GraphCasts’ extreme rainfall detection has been further examined with Critical Success Index (CSI) and False Alarm Ratio (FAR) for three threshold benchmarks: 100, 200 and 300 mm. The findings revealed that GraphCast outperforms ECMWF and GFS in routine precipitation forecasting, achieving higher CC (0.57–0.65), lower RMSE (15.66–16.61 mm day−1), and near-perfect POD values (>0.98). It exhibited better performance in central and northern Bangladesh, where monsoon characteristics are more uniform compared to coastal and southeastern hilly regions. However, GraphCast tends to overestimate extreme rainfall events with lower CSI (0.4476–0.5170) and higher FAR (0.4809–0.5519) values. Overall, this study aims to highlight the potential of AI-based operational precipitation forecasting with a path open to integrating hybrid AI-physics frameworks for better extreme event prediction in future.