Deep Learning Weather Prediction Models Exhibit Outstanding Accuracy when Predicting Cold and Hot Spells over North America and Europe

The rise of deep learning weather prediction (DLWP) models promises to improve short- to mid-ranged weather forecasts out to 14 days. Deep learning models, however, are known in general to perform poorly in conditions that are represented sparsely in the training data and to generalize poorly out of the distribution of the training data. Translated to weather forecasting, this suggests that DLWP models are inaccurate when predicting extreme events that occur only rarely. These extreme events, however, are of highest interest when preventing danger and damage to societies. Here, we therefore inspect how state-of-the-art DLWP models compare to the numerical weather prediction (NWP) model from the European Center for Medium-Ranged Weather Forecasts (ECMWF) on extreme cold and hot spells over North America and Europe. Our results speak not only for DLWP forecasts under normal conditions, but also promise significant skill improvements when forecasting extreme events with DLWP models, emphasized most stongly on cold spells over North America. Similar but weaker trends are observed in cold spell conditions over Europe, as well as in hot spells over North America and Europe. In general, our findings encourage further research in data driven models, such as Pangu-Weather, GraphCast, Aurora, and ECMWF's AIFS. Notably, the advances in DLWP is directly related to decades of research on NWP models. In future research, we will explore the response of DLWP models to warmer climate scenarios that are expected in the later 21st century.