RESOLVING THE LEDGER
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Add as preferred source For all that day-to-day weather forecasts have improved, it remains a challenge to forecast events that might happen once in 1,000 years—like the deadliest heat waves.
Traditional supercomputer-based models can forecast these events, but they require a lot of time and energy. Meanwhile, newer forecasting models, based on artificial intelligence , are good at day-to-day forecasts but often fail to predict outlier events that weren't represented in their training data.
"AI weather and climate models are one of the great achievements of AI in science, but they're not magical—they fail on gray swans, the rarest and most extreme events," said Pedram Hassanzadeh, University of Chicago associate professor of geophysical sciences. "Detailed physics-based models can capture extremes, but they require prohibitively large amounts of time and energy."
An international team of researchers in the United States and France, co-led by members of Hassanzadeh's Climate Extremes Theory and Data Group, has developed a new hybrid method, published in Physical Review Letters , to solve the problem.
Their solution marries the efficiency of AI tools with the trustworthiness of traditional models to help predict the odds of rare events quickly and accurately while using far fewer resources.
"The power of this method," Hassanzadeh said, "is that it combines the strengths of both AI and traditional physics and is particularly effective for extreme events, which are the hardest to simulate and have the greatest societal impact."
Heat waves are one of the deadliest forms of extreme weather. In 2003, a heat wave led to roughly 70,000 deaths across Europe, and Russia suffered 56,000 deaths in 2010. This past June, nearly half of the United States—roughly 180 million people—experienced dangerous temperatures.
These waves are becoming increasingly frequent and severe, but the nature of outlier events makes them difficult to study and challenging to predict.
Forecasting has long relied on physics-based climate and weather models. They help us see how different conditions, like atmospheric pressure, might affect temperature and other variables over time. These models compute many different potential scenarios, which researchers use to see what is most likely to occur.
The trouble is that if you want to know the odds that Chicago will reach 90°F (32°C) in July—which is not uncommon—you wouldn't need to run many simulations before one landed on that temperature. But if you want to know the odds that it will reach 105°F (41°C), you'll need to try many more times before you see that extreme. Running that many simulations takes a lot of time and computational power.
Researchers can mitigate the issue by using a statistical technique called rare event sampling (RES), which speeds up the process by scoring conditions so the climate model can focus only on the most promising and ignore the rest. However, rare event sampling doesn't work well for short-duration events, like weeklong heat waves, rather than an entire season that is unusually hot overall.
To address this, the team came up with a new method named AI+RES, which boosts the scoring mechanism by adding AI's ability to predict which conditions are most likely to lead to shorter, rapidly developing extremes.
"After doing this iteratively, you eventually get to a bunch of simulations that do indeed capture whatever rare extreme event that you're interested in," said Alexander Wikner, Schmidt AI in Science Postdoctoral Fellow in Hassanzadeh's group and co-first author on the study. "The more you get, the better you can estimate the probability of that event, and that ultimately gives you a lot more certainty."
To test the method, the team ran 50,000 simulations using a traditional climate model to predict heat waves over areas of France and the U.S. Midwest. Their new AI+RES method gave nearly identical results using one-hundredth as many simulations.
Because this work was a proof of concept, they used a model that didn't take into account climate change, which adds another level of complexity. The researchers hope to test their method on models running under different climate change scenarios to see how results might shift as the planet warms.
Wikner notes that the method could also help generate rare-event data sets to train even better AI models, which would then speed up their method even further.
According to the scientists, the hybrid method could be applied to other severe weather, including tropical cyclones or extreme precipitation.
"What I like about this framework," Hassanzadeh said, "is that it's ready to be scaled. AI models trained on real weather observations are already built and in use, so the next step is to connect a state-of-the-art numerical weather or climate prediction model to one of them through this algorithm."
This would give decision-makers access to accurate information they need, like the frequency of strong storms and heat waves in the current and future climate at regional scales—such as over Texas, Florida or California.
"This is exactly the kind of information federal, state and local governments need as a first step for climate adaptation and mitigation planning," added Hassanzadeh. "It's very exciting that we could be scaling this up into real-world models that directly provide such information to the public and to policymakers."
Amaury Lancelin et al, AI-Boosted Rare Event Sampling to Characterize Extreme Weather, Physical Review Letters (2026). DOI: 10.1103/b1gc-9c2q
Journal information: Physical Review Letters
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