An LLM Framework for Regional Climate Services: Integrating Climate Knowledge and Ensemble Projections

Abstract Recent advances in generative AI have enabled the integration of scientific knowledge into natural language interfaces. However, existing large language models (LLMs) lack domain‐specific expertise and cannot directly utilize simulation data essential for risk assessment. This study develops a climatology‐specific LLM fine‐tuned on climate science literature and augmented with ensemble projection data set, aiming to support local governments and small enterprises in climate adaptation planning as a preliminary support tool. The framework combines domain‐specific fine‐tuning, retrieval‐augmented generation (RAG) for scientific documents and local guidelines, and future projection data. The developed model demonstrated highly competitive performance compared to general‐purpose LLMs such as Swallow 70B on climate‐specific benchmarks in both English and Japanese, particularly in the categories of “Impacts, adaptation, and vulnerability” and “Mitigation.” A case study in Kumagaya City, Japan, demonstrated that the model can quantitatively update heat‐mitigation measures using probabilistic projections and dynamically retrieved local constraints, generating data‐informed baselines to facilitate human deliberation under multiple warming scenarios. The proposed approach bridges the gap between climate science and the early stages of adaptation planning by enabling AI‐driven access to both textual and numerical climate knowledge. By lowering technical barriers to expert‐level analysis, it facilitates inclusive and data‐driven climate services for diverse stakeholders. This study represents the first systematic demonstration of an LLM that integrates ensemble projection data to support regionally grounded adaptation planning, contributing to the development of next‐generation climate services that enhance local resilience.