A Targeted Correction for Arctic Oscillation Biases in Seasonal Forecasts

Seasonal prediction of Eurasian winter surface air temperature (SAT) is fundamentally limited by systematic biases in representing Arctic Oscillation (AO) teleconnections. Across the APEC Climate Center (APCC) Multi-Model Ensemble (MME), most component models misrepresent the impact of the AO on SAT, leading to degraded skill in Eurasian forecasts. To address this deficiency, we applied an AO correction method that replaces the distorted AO–SAT relationship simulated by models with one constrained by observations. The correction systematically enhanced winter SAT prediction skill across all APCC MME models, demonstrating that the improvement is not model-specific but generalizable across the ensemble. Importantly, the magnitude of the SAT ACC improvement showed a strong inverse relationship with intrinsic AO skill: models with the weakest ability to capture AO variability experienced the greatest gains (correlation r = − 0.65). This finding highlights that AO correction functions not as a uniform adjustment, but as a targeted remedy for a structural deficiency common to current generation seasonal forecast models. By demonstrating that a targeted correction of the AO yields systematic skill improvements—particularly in models with the poorest representation of Arctic variability—this study establishes a generalizable and physically-motivated post-processing framework for enhancing seasonal climate prediction.