Artificial intelligence is becoming increasingly visible across society, but its role in weather forecasting and climate science is far more practical and specialized than the hype surrounding consumer AI tools might suggest. In these fields, AI primarily refers to machine learning rather than large language models. Machine learning systems are designed to identify patterns in large datasets and use those patterns to make predictions, helping scientists improve forecasting accuracy, reduce computational costs, and enhance climate research.
Machine learning models are trained using vast amounts of historical data. By analyzing relationships between variables such as temperature, air pressure, wind, precipitation, and humidity, these systems learn how weather conditions evolve over time. Unlike traditional weather models, which rely on solving complex physical equations across millions of locations, machine-learning-based systems focus on recognizing patterns from previous observations. This allows AI weather models to run dramatically faster while consuming far less computing power.
Major technology companies, including Google, Microsoft, Nvidia, and Huawei, have developed machine learning weather models that compare favorably with conventional forecasting systems. The European Centre for Medium-Range Weather Forecasts (ECMWF) introduced its Artificial Intelligence Forecasting System (AIFS) in 2025 to complement its long-established forecasting model. The benefits are substantial: forecasts can be generated in minutes rather than hours while using only a fraction of the energy required by traditional approaches. This increased efficiency is particularly valuable when running large ensembles of simulations to evaluate multiple possible weather outcomes.
Despite these advantages, machine learning models also have important limitations. Because they learn from historical examples, they can struggle with rare or unprecedented events. Extreme weather events such as record-breaking storms, heat waves, or floods may not be well represented in training datasets. As a result, AI systems sometimes underestimate the frequency or intensity of extreme conditions. Researchers have found that machine learning weather models often smooth out unusual events, making them less reliable for forecasting the most severe weather situations where accurate predictions are most critical.
These limitations are even more significant in climate science. Weather forecasting focuses on predicting conditions over days, while climate modeling seeks to understand long-term changes over decades or centuries. Climate scientists frequently ask questions that have no historical precedent, such as how future temperatures will respond to increased greenhouse gas emissions. Because these scenarios extend beyond existing observations, physical laws remain essential. As a result, researchers generally do not use machine learning to replace climate models entirely. Instead, they integrate machine learning into specific parts of larger physics-based systems.
One example is the Climate Modeling Alliance (CliMA), which is developing a next-generation climate model that combines traditional physics with machine learning. Rather than replacing the entire model, machine learning is used for specific processes such as snow-cover calculations and cloud interactions where it can improve efficiency without sacrificing scientific reliability. This hybrid approach preserves physical constraints while benefiting from the speed and flexibility of machine learning.
Researchers are also using machine learning to improve model calibration, optimize parameters, and create lightweight “emulators” that replicate the behavior of much larger climate models. These emulators allow scientists to rapidly test new greenhouse gas emission scenarios without requiring extensive supercomputer resources. In addition, explainable machine learning techniques are helping scientists better understand how predictions are generated, reducing concerns about the “black box” nature of neural networks.
Overall, machine learning is becoming an increasingly valuable tool in weather and climate science. While it is not replacing physical models or scientific expertise, AI is helping researchers generate forecasts faster, improve computational efficiency, explore new climate scenarios, and extract greater value from growing environmental datasets. Its greatest contribution may not be replacing traditional methods but enhancing them in ways that accelerate scientific discovery while maintaining the physical foundations necessary for reliable prediction.