A new study reveals that weather forecasts are less accurate in poorer countries compared to wealthier ones. The research, conducted by Manuel Linsenmeier from Goethe University Frankfurt and Jeffrey Shrader from Columbia University, analyzed global weather forecasts from 1985 to 2020. They compared predictions from the European Centre for Medium-Range Weather Forecasts (ECMWF) with actual weather data, focusing on short-term temperature, precipitation, and surface pressure forecasts. The findings show that in high-income countries, forecasts are more accurate—sometimes even a seven-day forecast in a rich country is more precise than a one-day forecast in a low-income country. Despite overall improvements in weather forecasting since the 1980s, the gap between rich and poor countries has not narrowed much. Geography plays a role, as weather prediction is harder in the tropics, where many low-income countries are located. However, the study found that geography alone explains about two-thirds of the global differences in forecast accuracy. Other factors include the number and quality of weather observation systems. Poorer countries often have fewer weather stations and instruments that collect data less frequently, which can affect forecast accuracy. National meteorological services also vary in capacity. Only a few countries run their own global weather prediction models, while most rely on international centers. These forecasts are then adjusted using local data and expertise, which requires skilled staff, reliable data, and strong computing resources. High-income countries are more likely to provide official forecasts to the World Meteorological Organization (WMO), with 76% of them submitting forecasts for their capital cities, compared to just 19% of low-income countries. Linsenmeier highlights that while some geographic challenges are hard to change, improvements are possible through better observation networks, advanced models, and stronger national weather services. International cooperation is key, as weather data is a global resource that benefits everyone. Better forecasts in underserved areas not only help local communities but also improve accuracy worldwide, as global models rely on data from all regions. Linsenmeier is also leading research on the social and economic value of weather information. In a recent study, he and colleagues found that more accurate temperature forecasts, especially during heatwaves, can reduce mortality. As climate change increases extreme heat events, improving forecast accuracy becomes even more critical. Currently, he is working with a German health insurer to study how weather warnings can help protect vulnerable groups from the health impacts of rising temperatures.