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Here we introduce a demand-driven framework designed to implement climate services in the health sector, with a particular focus on the Caribbean region. Climate services are essential for supporting informed decision-making and response strategies in relation to climate-related health risks. Through collaborative efforts, we are co-producing a climate-driven dengue early warning system (EWS) to target vector-borne diseases effectively. While challenges exist in implementing such systems, EWSs provide valuable tools for managing epidemic risks by predicting potential disease outbreaks in advance. The scarcity of operational climate tools in the health sector underscores the need for increased investment and strategic implementation practices. To address these challenges, a demand-driven framework is proposed, emphasizing strategic planning focused on health intervention development, partnership building, data, communication, human resources, capacity building, and sustainable funding. This framework aims to integrate climate services seamlessly into health systems, thereby enhancing public health resilience and facilitating well-informed decision-making to effectively address climate-sensitive diseases. 

Abstract Extreme weather events have devastating impacts on human health, economic activities, ecosystems, and infrastructure. It is therefore crucial to anticipate extremes and their impacts to allow for preparedness and emergency measures. There is indeed potential for probabilistic subseasonal prediction on time scales of several weeks for many extreme events. Here we provide an overview of subseasonal predictability for case studies of some of the most prominent extreme events across the globe using the ECMWF S2S prediction system: heatwaves, cold spells, heavy precipitation events, and tropical and extratropical cyclones. The considered heatwaves exhibit predictability on time scales of 3–4 weeks, while this time scale is 2–3 weeks for cold spells. Precipitation extremes are the least predictable among the considered case studies. ­Tropical cyclones, on the other hand, can exhibit probabilistic predictability on time scales of up to 3 weeks, which in the presented cases was aided by remote precursors such as the Madden–Julian oscillation. For extratropical cyclones, lead times are found to be shorter. These case studies clearly illustrate the potential for event-dependent advance warnings for a wide range of extreme events. The subseasonal predictability of extreme events demonstrated here allows for an extension of warning horizons, provides advance information to impact modelers, and informs communities and stakeholders affected by the impacts of extreme weather events.