# ClimAgent: LLM as Agents for Autonomous Open-ended Climate Science Analysis Source: [ClimAgent: LLM as Agents for Autonomous Open-ended Climate Science Analysis]() Collection index: [llms.txt]() Data updated: 2026-06-02T05:00:05.013Z Authors: Hao Wang, Jindong Han, Wei Fan, Hao Liu Published: 2026-04-18T09:10:21.000Z ## Abstract Climate research is pivotal for mitigating global environmental crises, yet the accelerating volume of multi-scale datasets and the complexity of analytical tools have created significant bottlenecks, constraining scientific discovery to fragmented and labor-intensive workflows. While the emergence Large Language Models (LLMs) offers a transformative paradigm to scale scientific expertise, existing explorations remain largely confined to simple Question-Answering (Q&A) tasks. These approaches often oversimplify real-world challenges, neglecting the intricate physical constraints and the data-driven nature required in professional climate science.To bridge this gap, we introduce ClimAgent, a general-purpose autonomous framework designed to execute a wide spectrum of research tasks across diverse climate sub-fields. By integrating a unified tool-use environment with rigorous reasoning protocols, ClimAgent transcends simple retrieval to perform end-to-end modeling and analysis. To foster systematic evaluation, we propose ClimaBench, the first comprehensive benchmark for real-world climate discovery. It encompasses challenging problems spanning 5 distinct task categories derived from professional scenarios between 2000 and 2025. Experiments on ClimaBench demonstrate that ClimAgent significantly outperforms state-of-the-art baselines, achieving a 40.21% improvement over original LLM solutions in solution rigorousness and practicality. Our code are available at https://github.com/usail-hkust/ClimAgent. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Open-ended Climate Analysis on ClimaBench 100-sample Human-Evaluated Subset](): Open-ended Climate Analysis; ClimaBench 100-sample Human-Evaluated Subset; AE: 9.34. - [Climate Discovery on ClimaBench 2025](): Climate Discovery; ClimaBench 2025; AE Score: 8.95. - [Climate Discovery on ClimaBench 2000–2024](): Climate Discovery; ClimaBench 2000–2024; AE (Absolute Error): 9.22. ## Original sources and implementation [Original paper]() [PDF]()