Artificial intelligence (AI) has emerged as a promising alternative to traditional numerical weather prediction (NWP) models. However, the mismatch between training and operational input data limits its practical application in real-time forecasting. This study overcomes this challenge by fine-tuning the FuXi model using the Global/Regional Assimilation and Prediction Enhanced System (GRAPES) reanalysis data, resulting in an adapted version termed FuXi-GRAPES (FuXi-G). FuXi-G was evaluated over the full calendar year 2021. Compared to its baseline configuration, FuXi-G demonstrates marked improvements in forecast skill, particularly in the Southern Hemisphere and the tropics, achieving temporal and spatial accuracy that rivals or slightly surpasses that of NCEP operational forecasts. The FuXi-G model also exhibited a notable reduction in error propagation, outperforming NCEP forecasts by Day 5 and alleviating seasonal forecast biases in the Southern Hemisphere. A case study of Typhoon Khanun underscored the model’s enhanced ability to predict high-impact weather events, including a critical directional shift of the typhoon associated with the breakdown of the subtropical high. These results suggest that fine-tuning AI models can substantially improve forecasting accuracy while avoiding the computational burden of retraining on new datasets, providing a scalable approach for implementing AI models in operational weather forecasting.

The North Atlantic Oscillation (NAO) is a major atmospheric mode in the Northern Hemisphere, characterized by frequent fluctuations in sea level pressure (SLP) across the North Atlantic sector. In the development and evolution of the NAO, various dynamic physical processes such as the El Niño–Southern Oscillation (ENSO) and Madden–Julian Oscillation (MJO) influence it to different extents. Previous studies using numerical models or deep learning models for daily NAO forecasts have not accounted for the impact of these dynamic physical processes, making accurate and stable NAO forecasting still a challenge. In this study, the Varimax-Rotation Principal Component Analysis (PCA) and data-driven causal inference are used to identify key dynamic physical processes linked to the NAO. Based on these, a deep learning model called the NAO-Causal Weighted Model (NAO-CWM) is developed, which incorporates causal relationships to assign different weights to these processes, providing effective daily forecasts with a lead time of 1–14 days. Evaluation results show that NAO-CWM outperforms the advanced numerical models, offering reliable NAO forecasts and a better capturing of NAO variation trends.