Hima-Net: Deep Learning Enhancement of ECMWF S2S Winter Precipitation Forecasts over Northern India

Seasonal climate forecasts are critical for disaster management across the fragile Himalayan ecosystem, particularly during winter. However, these forecasts often exhibit strong spatial and temporal biases that reduce their reliability for predicting extremes at longer lead times. Traditional postprocessing methods such as quantile mapping and linear scaling assume stationarity and have limited ability to capture complex spatiotemporal error structures. To address these limitations, this study introduces Hima-Net (Himalayan-Net), a hybrid deep learning model that combines U-Net and Conv-LSTM architectures. Hima-Net is designed to improve the skill of sub-seasonal-to-seasonal (S2S) daily precipitation forecasts from the ECMWF S2S system by learning season-specific spatial and temporal patterns in forecast errors. The model is trained with a loss function that jointly emphasizes magnitude and correlation, enhancing its ability to represent the distribution and evolution of precipitation across lead times. Evaluation using metrics such as root mean square error (RMSE) and anomaly correlation coefficient (ACC) shows that Hima-Net consistently outperforms the raw forecasts across lead times over the Himalayan region. These findings demonstrate the potential of deep learning–based postprocessing to better capture and enhance spatial and temporal forecast patterns, offering a promising pathway for more accurate wintertime precipitation forecasts over the complex terrain of the Himalayas.