Robert Monjo
Director of Research and Innovation at the Foundation for Climate Research and Associate Professor in the Department of Algebra, Geometry and Topology at the Complutense University of Madrid (UCM).
The significance of this work lies in the use of probabilistic post-processing (PBC), which learns to correct the systematic errors in the best existing models—both dynamic and AI-based—without modifying them. It is noteworthy that the PBC method won the ECMWF’s international AI Weather Quest competition in 2025 for all variables and time horizons, outperforming the operational models of six national meteorological centers (ECMWF, NOAA, JMA, KMA, ECCC, HMCR). The most significant improvement is seen in precipitation for the 3- and 4-week time horizons, where the models’ direct output exhibits a very high systematic bias relative to the climatological reference. The code is open-source, and the approach is applicable to any weather prediction model, which greatly expands its practical scope.
It should be noted, however, that a new atmospheric model is not built from scratch. The PBC method is a post-processing tool that must be applied to the base models. If the dynamic models improve, PBC can adapt, but if they fail in certain complex and rare situations, the PBC method cannot correct this. Another limitation of the study is that the validation uses ERA5 as the reference truth, which has its own uncertainties since it is another model that simulates precipitation (it does not assimilate actual precipitation observations, but it does assimilate temperature, humidity, and wind).
On the other hand, although the leap in precipitation predictive ability is notable (it goes from negative to positive ability), the relative improvement is misleading because it is akin to “dividing by zero.” In other words, “doubling the ability” from a modest starting point does not necessarily mean it is a “useful forecast.” The subs seasonal predictability window remains inherently challenging, ensuring many more years of work in mathematical and meteorological research.