How to systematically develop an effective AI-based bias correction model?

Numerical weather prediction (NWP) is crucial in weather forecasting, providing indispensable guidance across temporal scales from nowcasting to seasonal forecasting (Bauer et al., 2015). As society becomes more dependent on accurate forecasts, there is an increasing demand for high-quality predictions, particularly in extreme events such as heat waves and cold surges, which can have severe social and economic impacts(Br as et al., 2023; Miao et al., 2024). Furthermore, atmospheric forecasts serve as critical boundary conditions for coupled Earth system models, where their accuracy directly governs the predictive capabilities of oceanographic and cryospheric simulations through dynamic coupling mechanisms. While the ECMWF's Integrated Forecasting System (IFS) represents the state-of-the-art in global operational prediction (Molteni et al., 1996), persistent systematic biases still exist, which arise from three fundamental sources: (1) inadequate spatial resolution to resolve subgrid-scale processes (Mishra et al., 2021), (2) inherent limitations in physical parameterization schemes (Berner et al., 2017; Brenowitz & Bretherton, 2018), and (3) uncertainties in initial/boundary condition specification (Peng & Xie, 2006). Current bias correction paradigms predominantly employ statistical postprocessing techniques, including uni-variate regression frameworks (Turco et al., 2017), adaptive filtering techniques (Chandramouli et al., 2022), and probabilistic calibration methods (Yumnam et al., 2022).