It's surprising that AI models trained on general data can accurately predict rare events like hurricanes. The reason is that the physical world is "forgiving"; extreme phenomena are governed by strong physical structures and signatures that AI can learn effectively, even from a limited number of examples.

An AI model may not have seen a specific record-breaking hurricane before, but it has seen its components—intense wind, low pressure—in other storms globally. It predicts novel extreme events by composing these learned "local statistics" or "parts" into a new, plausible combination.

A predictive model minimizes error on a dataset (e.g., "apples fall") but can't extrapolate. True science requires descriptive models that capture underlying physics (e.g., gravity), allowing for extrapolation to new domains like the movement of planets.

AI's predictive power is based on identifying patterns in historical data. While effective when the future resembles the past, this makes it inherently unable to account for new inventions, crises, or paradigm shifts not represented in its training text. It predicts from old maps, not what will come next in a new world.

Counter-intuitively, successful weather and climate AI models are not trained on long-term data. They are trained to predict only the next six hours autoregressively. This surprisingly generalizes to stable rollouts predicting weather patterns for hundreds or thousands of steps into the future, enabling long-term forecasting from short-term training.

AI's advantage is superior pattern recognition, not magic. Since the physical laws governing weather are constant, an AI trained on vast historical data can identify subtle, previously unnoticed relationships between past and future events, leading to more accurate predictions than traditional models.

Unlike traditional models that make localized predictions, AI models analyze the planet's entire weather state simultaneously. This allows them to learn from large-scale structures, like a whole hurricane, using context from one side of a storm to improve predictions for the other side.

Most AI weather models project the Earth onto a flat rectangle, causing simulations to become unstable and "blow up" over long periods. By incorporating the planet's spherical geometry using Fourier Neural Operators, models like ForecastNet remain stable for long-term climate rollouts, effectively becoming climate models.

Traditional physics-based simulations can "blow up" when small errors compound, leading to absurd forecasts. AI models have a safer failure mode: when encountering uncertainty, they "regress to the mean," predicting the historical average weather, which is a more stable and less misleading outcome.