Early Warning Systems (EWS) represent a critical cornerstone of disaster risk reduction as they provide an essential foundation for protecting lives and livelihoods through the timely provision of actionable information. However, the efficacy of EWS is dependent not only on scientific robustness but also on seamless integration across disciplines, from disaster risk knowledge and hazard detection to communication strategies and community response. Subsequently, these systems require innovative advancements across the warning chain to meet the ambitious targets outlined in the Early Warnings for All (EW4ALL) initiative action plan and the Sendai Framework towards multi-hazard, all-vulnerability, and impact-based EWS. This session aims to foster a dialogue on the implementation and methodological innovations surrounding EWS, particularly between researchers working toward more effective, inclusive, and actionable EWS.

This interdisciplinary session invites contributions from a wide range of disciplines and sectors involved with the full spectrum of EWS development and implementation, including but not limited to natural hazards science, atmospheric and hydrologic research, social sciences, and disaster management practice. We encourage submissions addressing the following key themes and sharing of lessons from successes and failures:
● Early warning and anticipatory action: Frameworks and multi-stakeholder implementation in translating early warnings/EWS into effective disaster response and preparedness mechanisms;
● Impact-based approaches: methodologies and approaches for design and implementation of impact-based EWS;
● Technological innovations: advances in AI, machine learning, Earth observation, IoT and other cutting-edge technologies in components of EWS;
● Risk communication and community engagement: strategies that integrate behavioral and psychological insights, building trust, and ensure effective warning communication and dissemination, particularly at the community level;
● Data integration and system interoperability: approaches to integrate diverse data sources that address challenges in cross-agency data sharing and platform integration.