Abstract The surface energy budget governs Arctic sea‐ice growth/melt, yet observations are sparse, and reanalysis data sets suffer from systematic biases. Here, we train a neural network with observational data to bias‐correct hourly ERA5 fluxes over Arctic ice‐covered regions (≥70°N; sea‐ice concentration >80%) for 1994–2024. Training data cover two full seasonal cycles and different sea‐ice regimes. The neural network reduces RMSE for net shortwave radiation by ∼40%, downward longwave radi