Weather forecasts are fundamentally uncertain, so predicting the range of probable weather scenarios is crucial for important decisions, from warning the public about hazardous weather to planning renewable energy use. Traditionally, weather forecasts have been based on numerical weather prediction (NWP)¹, which relies on physics-based simulations of the atmosphere. Recent advances in machine learning (ML)-based weather prediction (MLWP) have produced ML-based models with less forecast error than single NWP simulations^2,3. However, these advances have focused primarily on single, deterministic forecasts that fail to represent uncertainty and estimate risk. Overall, MLWP has remained less accurate and reliable than state-of-the-art NWP ensemble forecasts. Here we introduce GenCast, a probabilistic weather model with greater skill and speed than the top operational medium-range weather forecast in the world, ENS, the ensemble forecast of the European Centre for Medium-Range Weather Forecasts⁴. GenCast is an ML weather prediction method, trained on decades of reanalysis data. GenCast generates an ensemble of stochastic 15-day global forecasts, at 12-h steps and 0.25° latitude–longitude resolution, for more than 80 surface and atmospheric variables, in 8 min. It has greater skill than ENS on 97.2% of 1,320 targets we evaluated and better predicts extreme weather, tropical cyclone tracks and wind power production. This work helps open the next chapter in operational weather forecasting, in which crucial weather-dependent decisions are made more accurately and efficiently.

天气预报从根本上说是不确定的，因此预测可能的天气情景范围对于重要决策至关重要，从警告公众危险天气到规划可再生能源的使用。传统上，天气预报基于数值天气预报 （NWP）¹，它依赖于基于物理的大气模拟。基于机器学习 （ML） 的天气预报 （MLWP） 的最新进展已经产生了基于 ML 的模型，其预报误差比单个 NWP 模拟要小^2,3。然而，这些进步主要集中在单一的确定性预测上，这些预测无法代表不确定性和估计风险。总体而言，MLWP 的准确性和可靠性仍然不如最先进的 NWP 集合预报。在这里，我们介绍了 GenCast，这是一个概率天气模型，比世界上顶级的业务中期天气预报 ENS（欧洲中期天气预报中心⁴ 的集成预报）具有更高的技能和速度。GenCast 是一种 ML 天气预报方法，基于数十年的再分析数据进行训练。GenCast 在 8 分钟内以 12 小时为步长，以 0.25° 的经纬度分辨率为 80 多个表面和大气变量生成一组随机 15 天全球预报。在我们评估的 1,320 个目标中，97.2% 的目标比 ENS 具有更强的技能，并且可以更好地预测极端天气、热带气旋路径和风力发电。这项工作有助于开启业务天气预报的新篇章，在这个篇章中，可以更准确、更高效地做出关键的天气相关决策。