Artificial intelligence is transforming the way societies observe, predict and respond to dangerous weather—but a new analysis warns that the same technology capable of making forecasts faster and more precise could also deepen the world’s existing climate inequalities. In a study published in npj Climate Action, Mozaffari, Duarte, Teckentrup and colleagues examine the rapidly expanding role of AI in weather and climate information, highlighting a global paradox: the communities most exposed to floods, droughts, heatwaves and storms are often the least able to access the digital infrastructure, data and expertise needed to benefit from AI-powered prediction.
Modern weather forecasting already depends on enormous volumes of information. Satellites, weather stations, ocean buoys, aircraft sensors and radar systems continuously measure temperature, humidity, wind, pressure and precipitation. Traditional numerical weather prediction then uses physical equations to simulate how the atmosphere evolves. These models have become dramatically more capable, but they require immense computing power and can take substantial time to run. AI introduces a different approach. Machine-learning systems can identify patterns in historical observations and generate forecasts directly, sometimes producing predictions in seconds rather than hours. That speed could be crucial when governments have only a short window to issue warnings or evacuate vulnerable populations.
The most powerful AI weather systems combine several technical strategies. Neural networks can learn relationships between atmospheric variables across vast geographical areas, while “foundation” models trained on decades of reanalysis data can generate forecasts without calculating every atmospheric process from first principles. Some systems use hybrid methods, combining machine learning with physical constraints so that predictions remain consistent with conservation laws and known dynamics. AI can also improve “downscaling,” converting broad regional forecasts into neighborhood-level estimates of rainfall, wind or heat. For a city preparing for flash floods, that additional resolution could mean the difference between a general warning and a precise alert identifying which roads, hospitals or homes face the greatest danger.
Yet forecast quality is only one part of the equation. A warning has value only when it reaches people in a form they can understand and when they have the means to act. AI-generated information may be delivered through smartphone applications, online dashboards, automated messages or digital platforms, but access to reliable electricity, mobile networks, affordable data and internet-connected devices remains deeply uneven. Rural communities and low-income households may also lack the local agencies, emergency shelters or financial resources needed to respond. A highly accurate prediction cannot prevent harm if residents receive it too late, cannot interpret it, or are unable to leave danger.
The inequality problem begins long before an AI model produces its first forecast. Machine-learning systems depend on data, and data are not distributed evenly across the planet. Wealthier countries generally operate denser networks of weather stations, radar installations and satellites, creating detailed records for training and validating algorithms. Many low-income regions have sparse or deteriorating observation systems, particularly in rural areas and across parts of Africa, Asia and small island states. When models are trained primarily on data from well-monitored regions, they may perform less reliably in places with different climates, landscapes or seasonal patterns. This can create a feedback loop in which the areas most in need of better information remain the least represented in the datasets used to build it.
Climate change makes that limitation more serious. AI systems learn from historical examples, but the atmosphere is moving into conditions that have no exact precedent in the observational record. Extreme heat, compound droughts, intense rainfall and rapidly strengthening tropical cyclones may occur at frequencies or combinations that were rare in the past. A model that recognizes patterns efficiently can still struggle when those patterns shift beyond its training experience. Researchers therefore face a central technical challenge: making AI forecasts adaptable to a changing climate while communicating uncertainty honestly. A prediction should not be treated as an unquestionable answer; it is an estimate shaped by data quality, model design and the unpredictability of atmospheric processes.
The study’s warning extends beyond forecasting to the ownership and governance of climate information. The most advanced AI systems are often developed by a small number of technology companies, research institutions and governments with access to specialized chips, massive datasets and highly skilled engineers. If critical forecasting tools become proprietary, poorer countries could depend on external providers for information essential to public safety. Commercial systems may also prioritize profitable markets, high-value infrastructure or users able to pay for premium services. This raises questions about whether climate information should be treated as a private product or as a public good, comparable to clean air, emergency broadcasting or basic health data.
There are ways to prevent AI from becoming another mechanism of exclusion. Investment in national and regional weather services, open observation networks and public-interest computing could help broaden access to the technology. International data-sharing agreements would allow under-monitored regions to contribute to and benefit from global forecasting systems. Models should be evaluated not only by their average accuracy, but also by how well they perform across different countries, climates, languages and socioeconomic settings. Local experts and communities must be involved in designing warning systems, because technical precision does not guarantee cultural relevance. A flood alert written in an unfamiliar language or delivered through an inaccessible platform may fail even if the underlying forecast is excellent.
Researchers also emphasize the importance of transparency. Users need to know what data an AI system was trained on, how often it is updated, where its predictions are reliable and when uncertainty is high. Independent testing can reveal whether a model systematically performs worse in particular regions or during specific hazards. Human expertise remains essential, especially for interpreting unusual events and translating forecasts into decisions. The strongest systems are likely to be collaborative rather than fully automated, pairing rapid machine-generated predictions with meteorologists, disaster managers, local authorities and community organizations that understand conditions on the ground.
AI could ultimately help close the climate-information gap, but only if access and accountability are built into its development from the beginning. Faster forecasts may support earlier evacuations, smarter water management, more resilient agriculture and better preparation for extreme heat. Without equitable infrastructure and public governance, however, the technology could concentrate lifesaving knowledge in the hands of those already best protected from climate hazards. The message of the npj Climate Action analysis is therefore both urgent and practical: the future of AI in weather and climate science will not be judged solely by how accurately it predicts the next storm, but by who receives that prediction, who can trust it and who has the power to act.
Subject of Research: The role of artificial intelligence in weather and climate information, including its potential to improve forecasting and its implications for global inequality.
Article Title: The rise of AI in weather and climate information and its impact on global inequality
Article References: Mozaffari, A., Duarte, A., Teckentrup, L. et al. “The rise of AI in weather and climate information and its impact on global inequality.” npj Climate Action (2026). https://doi.org/10.1038/s44168-026-00412-z
Image Credits: AI Generated
DOI: 10.1038/s44168-026-00412-z
Keywords: Artificial intelligence, weather forecasting, climate information, global inequality, climate change, machine learning, extreme weather, early warning systems, climate justice, meteorology
