When disaster looms, timing is everything. Without enough warning to effectively implement emergency planning, even the most well-resourced cities can be devastated by extreme weather.

Take the power crisis that struck Texas in February 2021 for example. Three back-to-back winter storms—including winter storm Uri—knocked out electricity and heat for millions, contributing to 246 deaths. If utilities had had more time to shore up natural gas supplies, reinforce infrastructure, and warn their customers, the toll may not have been so high.

The problem is that predicting the weather two to six weeks out is notoriously difficult. Past the two-week mark, small differences in current atmospheric conditions can lead to dramatically different weather outcomes, making a single, deterministic forecast unreliable. Meteorologists instead rely on probabilistic forecasting, which uses multiple model simulations to estimate the likelihood of different outcomes.

But these forecasts have their own limitations. As models simulate weather over successive time steps, errors can accumulate, reducing their accuracy over several weeks. Even the gold-standard numerical models from the European Centre for Medium Range Weather Forecasts (ECMWF) and standout AI models like Google DeepMind’s WeatherNext family struggle with this “subseasonal” range.

A new “wrapper” could help with that. This post-processing tool, detailed in a paper published today in the journal Nature, uses machine learning to correct systemic errors in forecast models and improve the accuracy of their subseasonal outlooks.

In ECMWF’s 2025 real-time forecasting competition, the tool produced the most accurate global forecasts for all weather variables and forecast periods, outperforming numerical models from six operational forecasting centers, an international ensemble of numerical models, ECMWF’s own AI model, and forecasting systems from 34 competing teams around the world.

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