  Global medium-range weather forecasting is critical to decision-making across
many social and economic domains. Traditional numerical weather prediction uses
increased compute resources to improve forecast accuracy, but cannot directly
use historical weather data to improve the underlying model. We introduce a
machine learning-based method called "GraphCast", which can be trained directly
from reanalysis data. It predicts hundreds of weather variables, over 10 days
at 0.25 degree resolution globally, in under one minute. We show that GraphCast
significantly outperforms the most accurate operational deterministic systems
on 90% of 1380 verification targets, and its forecasts support better severe
event prediction, including tropical cyclones, atmospheric rivers, and extreme
temperatures. GraphCast is a key advance in accurate and efficient weather
forecasting, and helps realize the promise of machine learning for modeling
complex dynamical systems.
