Advancing early warning systems: AI applications, challenges and emerging insights

Abstract The increasing frequency and intensity of extreme weather events driven by climate change lead to stronger impacts and fatalities, affecting more communities every year. Early warning systems (EWS) are essential adaptation tools designed to provide timely and actionable warnings of natural hazards such as heatwaves, floods, droughts and wildfires. The rapid development of artificial intelligence (AI), including machine learning (ML) and sophisticated deep learning models, together with the availability of large Earth observation data sets, offers a unique opportunity to improve the current capabilities of EWS. This review provides a comprehensive overview of existing AI-based methods applied to some of the most devastating natural hazards worldwide. We identify key gaps in the current landscape, not only geographically, but also in the application to specific natural hazards. While ML has advanced significantly in applications for flood and wildfire detection, its use for drought and heatwave prediction remains comparatively limited and underexplored. Differences among the manifestations of different natural hazards, how we can observe them (and the types of data) and how they are traditionally forecast or monitored, are limiting factors for the integration of AI-based solutions within operational EWS, particularly in regions and for hazard types that are most in need of robust early warning capabilities. Nevertheless, the tremendous effort in improving data-driven models for weather forecasts as well as the development of AI techniques for capturing complex environmental dynamics, creates a promising pathway to strengthening EWS. By addressing current gaps, such as regional coverage, data availability and hazard representation, AI can significantly enhance the precision and timeliness of EWS, ultimately contributing to more effective, inclusive and proactive disaster risk management.