Climate projections, reanalyses and Earth observation products are increasingly available, but their spatial resolution, biases, uncertainties and limited connection to sector-specific needs often constrain their direct use in societal risk and impact assessments. At the same time, advances in artificial intelligence, statistical modelling and data integration offer new opportunities to translate climate information into actionable indicators of hazards, exposure, vulnerability and risk.
This session invites inter- and transdisciplinary contributions that connect high-resolution climate information with impacts on human health, agriculture and food systems, water resources and vulnerable populations. Relevant topics include statistical and machine-learning downscaling; integration of climate, environmental, health and socioeconomic data; satellite-derived exposure indicators; climate-sensitive health outcomes; crop and hydrological impact modelling; extreme, compound and cascading risks; vulnerability mapping; uncertainty propagation; early-warning systems; and the co-development of climate services and adaptation strategies.
We particularly welcome studies that evaluate whether climate information is fit for its intended societal application, connect methods and evidence across disciplines, compare alternative modelling or data-integration approaches, and address challenges faced by data-scarce and highly vulnerable regions. Contributions that address the needs of, collaborate with, or generate relevant information for public-health agencies, agricultural and water-management organizations, education systems, humanitarian actors, local communities and policy stakeholders are especially encouraged.