Season‐Net: A Deep Learning Framework for Bias Correction of Seasonal Forecasting Models

Abstract Seasonal climate forecasts play a crucial role in decision‐making across sectors like agriculture, energy, and disaster management. However, these forecasts often exhibit spatially structured biases that undermine their reliability, but this structure also enables more effective bias correction, particularly improving performance in predicting temperature extremes. Traditional bias correction methods such as quantile mapping (QM) and linear scaling (LS) are limited by assumptions of stationarity and their inability to capture complex spatiotemporal patterns. To address these challenges, we introduce Season‐Net, a hybrid deep learning framework combining U‐Net and ConvLSTM architectures. Season‐Net is used to perform bias correction on seasonal daily temperature forecasts from the Met Office (GloSea6) and Météo‐France (System 8) by learning season‐specific spatial and temporal dependencies in a unified architecture. The model is trained with a novel sliding‐window quantile mapping loss function that introduces temporal awareness into the quantile mapping process, enhancing its ability to capture temperature distribution and evolution. Evaluations across No