Accurate tropical cyclone intensity forecasts using a non-iterative spatiotemporal transformer model

Accurate forecasting of tropical cyclone (TC) intensity—particularly during periods of rapid intensification and rapid weakening—remains a challenge for operational meteorology, with high-stakes implications for disaster preparedness and infrastructure resilience. Recent advances in machine learning have yielded notable progress in TC prediction; however, most existing systems provide forecasts that degrade rapidly in extreme regimes and lack long-range consistency. Here we introduce TIFNet, a transformer-based forecasting model that generates non-iterative, 5-day intensity trajectories by integrating high-resolution global forecasts with a historical-evolution fusion mechanism. Trained on reanalysis data and fine-tuned with operational data, TIFNet consistently outperforms operational numerical models across all forecast horizons, delivering robust improvements across the weak, strong, and super typhoon categories. In rapid intensification cases—long regarded as the most difficult to forecast—TIFNet reduces forecast error by 27–48% relative to current operational baselines. These results represent a substantial advance in artificial-intelligence-based TC intensity forecasting, especially under extreme conditions where traditional models consistently underperform.