What if we could predict the world’s most dangerous weather events—not days, but weeks in advance? Extreme events like heat waves, hurricanes, and floods cause massive loss of life and billions in damage, but they’re also some of the hardest events for traditional weather forecasting to predict.

In this episode, Assoc. Prof. Pedram Hassanzadeh of the University of Chicago explains why forecasting extreme weather has long pushed science to its limits—and how a new wave of AI models could transform the field at a time when climate change is making these events more common. By learning directly from decades of atmospheric data, these systems can generate forecasts faster, more cheaply, and in some cases more accurately than traditional models—even to predict freak ‘gray swan’ weather events no one has ever seen.

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