这篇论文把六个AI天气模型和传统模型放在一起比了10-15天的高温预报，发现AI能追平但会糊掉极端值，搞气象预测的值得看看。
arXiv论文评估了Pangu-Weather、FuXi、ArchesWeather、AIFS、GraphCast和Aurora六种深度学习天气仿真器，在10-15天超前时间下预测全球近地表温度和极端高温的表现。结果显示多个仿真器在确定性温度技能上媲美甚至超过物理模型，但存在频谱模糊问题。所有模型对极端高温都有一定预测技能，但多数低估峰值强度，IFS的召回率高于所有仿真器。研究凸显AI提升延伸期温度预测的潜力，也指出可靠预警的挑战。
Weather Emulators at the Frontier of Heat Extremes Predictability
Atmospheric predictability declines rapidly beyond the next ten days, such that forecasts at longer lead times primarily convey large-scale trends rather than specific states. Yet in a warming world, improving early warnings of extreme heat is an increasingly critical challenge. Here we evaluate six state-of-the-art deep learning weather emulators - Pangu-Weather, FuXi, ArchesWeather, AIFS, GraphCast and Aurora - alongside leading dynamical systems and statistical baselines in forecasting global near-surface temperature and extreme heat at lead times of 10-15 days. We find that several emulators rival or even surpass physics-based forecasts in deterministic temperature skill, but do so at the cost of reduced spectral fidelity, in a process widely known as blurring. While all models show some degree of predictive skill for extreme heat, most emulators under-represent peak intensities, and IFS recall is greater than that of any of the emulators. These results highlight both the emerging potential of AI to enhance extended range temperature prediction, and the remaining challenges in delivering reliable, actionable early warnings in a changing climate.