Global Scenario Reference Datasets for Climate Change Integrated Assessment with Machine Learning

Abstract. The deepening of global climate change research and increasingly complex integrated assessment methods generate large amounts of heterogeneous data. The rapid development of artificial intelligence (AI) models, particularly large language models (LLMs) and deep learning techniques, has enhanced the ability to handle vast data, providing new approaches and perspectives for climate analysis. To address the demand for multi-dimensional and comparable scenario design in climate change prediction and policy simulation, this study employs hybrid machine learning techniques to collect and process scenario data from existing literature, developing the Global Climate Scenario Reference datasets (GCSR). The GCSR incorporates data from approximately 90,000 articles across multiple temporal and spatial scales and extracts approximately 53,185 scenarios. With its large scale, extensive coverage, and detailed classification, the GCSR provides a robust foundation for climate change prediction, risk assessment, mitigation policy, and adaptation strategy planning, supporting scenario design in related fields.