Europe sees disasters coming. Can it act in time?

Europe is getting better at observing and modelling extreme weather, but translating that information into harm reduction remains a crucial challenge. When a forest fire breaks out, heavy rain threatens a town, or high winds put power lines at risk, emergency responders need to know where danger lies, how conditions might change and where resources are needed. As climate change heightens risks ranging from wildfires and flooding to drought and extreme heat, climate intelligence is increasingly vital to Europe’s ability to adapt. Half of the EU was experiencing some level of drought in summer 2026. By October, wildfires had burned more than 690,000 hectares of land, an area almost twice the 20-year average. Together, the figures highlight the scale of Europe’s climate risks and the growing need to prepare for their consequences. Europe’s challenge now is not to gather more climate data, but to make sense of what it already has, turning it into useful intelligence while there is still time to act. Operational decisions: knowing where to act The volume of climate data available from satellite imagery and meteorological forecasting has never been greater, but raw data alone cannot guide an emergency response. Geographic information systems (GIS) turn these datasets into actionable maps, helping track extreme weather and flag areas at risk of wildfires, flooding and storms. They can overlay weather information with details about terrain, infrastructure and emergency resources to give responders a clearer picture of the situation. Embedded artificial intelligence can analyse these massive, real-time datasets to identify patterns and draw out insights that might otherwise be missed. Simulations allow teams to explore how an incident might develop, helping emergency services anticipate changing conditions and prioritise their response. The aim is not to predict every eventuality, but to give field teams better information on where danger is greatest and where to act first. Preventative decisions: acting before disaster strikes Data technologies can also help authorities prepare for extreme weather before it occurs. While satellite imagery provides data on terrain, vegetation and climate changes, AI systems can analyse patterns across these sources, to help identify emerging risks. Scenario modelling also allows experts to explore how fires and floods might develop under different conditions, before they do. These tools do not eliminate uncertainty or replace human judgement. They can, however, help professionals identify vulnerable areas, compare possible scenarios and prioritise preventive action, particularly when resources are stretched. From modelling potential floodwater flows to identifying vegetation that could fuel a wildfire, climate intelligence can help authorities spot vulnerabilities early enough to act. Financial decisions: understanding what is at risk As extreme weather increasingly affects the European economy, the need for better climate intelligence extends beyond emergency services. Insurers, banks and energy companies share the same need to understand how floods, wildfires and other environmental hazards could affect their assets, infrastructure and financial positions. Scenario modelling helps diverse organisations explore what could happen under different conditions. By stress-testing their operations and investments against potential severe weather scenarios, they can identify vulnerabilities and make better-informed decisions before losses occur. Better information, better decisions? Across emergency response, prevention and financial risk management, the underlying challenge remains how to make complex datasets useful to the people who need to act on them. Technology cannot predict every disaster or eliminate uncertainty, but it is already helping public and private organisations understand risk earlier, make faster, more informed decisions and strengthen resilience against a wide range of climate risks. The value of climate intelligence lies not in how much data we collect, but in how effectively we turn it into practical decisions that reduce losses and save lives.