The “Last-mile Efforts” of Subseasonal Prediction and Services for Climate Resilience and Sustainability: Review and Outlook

Abstract Subseasonal predictions from 2 weeks to 2 months have made significant advancements over the past decade, driven by progress in physical understanding, climate modeling, computational capabilities, and artificial intelligence (AI). These predictions are increasingly in demand due to their potential to provide stakeholders with adequate lead time for effective disaster adaptation, mitigation, and resource management. However, there remain critical gaps in the engagement between prediction providers and service users. Providers often lack insight into the specific needs of users and do not have transferrable strategies to build trust through tailored evaluations and clear confidence levels, which often results in repeatedly devising approaches for each provider–user interaction. Further, users frequently struggle to interpret predictions and are hesitant to make decisions based on these uncertain outcomes. This paper attempts to make “last-mile efforts” by reviewing relevant literature, operational systems, and the most informative communications and engagement strategies with key sectors. It proposes a preliminary framework to standardize the approach for provider–user interaction in the context of subseasonal prediction and services, with potential applicability and extension to seamless prediction systems in the future. Lastly, we underscore future directions for subseasonal predictions, emphasizing the integration of dynamic climate modeling and AI-driven enhancements with large ensemble techniques to improve both reliability and confidence. This review is part of the United Nations Educational Scientific and Cultural Organization (UNESCO) International Decade of Sciences for Sustainable Development (2024–33) and contributes to the Seamless Prediction and Services for Sustainable Natural and Built Environment (SEPRESS) Program (2025–32), an initiative endorsed under this global framework.