Predicting the weather on a sub-seasonal scale (two to four weeks) using traditional physical models is currently not very feasible because the inherent uncertainty in observations rapidly degrades forecasts as the days go by. The study led by Hannah Guan, published in Nature, presents a new machine-learning-based data post-processing tool (Probabilistic Bias Correction, or PBC) that significantly improves sub-seasonal forecasts.


On a global scale, the study doubles the performance of the artificial intelligence (AI)-based forecasting system and improves it by more than 90% in traditional physical models. However, it should be noted that the positive impact is concentrated primarily in the tropical belt and remains quite limited in Europe, for example. The study highlights that the PBC method can also help improve the prediction of extreme events more than two weeks in advance. This is the primary goal of sub-seasonal forecasting: to anticipate events such as frosts, droughts, or storms weeks in advance, giving the agricultural, energy, and emergency sectors the time they need to take action.

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