The First 0.2° Resolution Global Continental Heat Flow Map: Advancing Fine-Scale Geothermal Modeling

Heat flow is a key parameter for revealing the Earth’s internal structure and the distribution of geothermal resources, playing a crucial role in geoscientific research and resource assessment. Current heat flow prediction typically relies on statistical regression or machine learning methods, which model and estimate heat flow by uncovering its relationships with geological and geophysical features. However, existing studies still face two core challenges: first, key modeling features generally suffer from low spatial resolution at the global scale, which limits the resolution of heat flow predictions; second, current machine learning methods often exhibit insufficient feature extraction and generalization capabilities under limited data conditions. To address these issues, various high-resolution geological feature data were systematically integrated and analyzed, and a Geographic-Climatic Proximity Feature Combination (GeoClimaProx) was proposed to balance effectiveness with accessibility. In addition, the Tabular Prior-data Fitted Network (TabPFN), an advanced tabular foundation model characterized by strong feature extraction and generalization capabilities, is introduced to enhance the performance of continental heat flow prediction. To validate the effectiveness of the proposed approach, comparative experiments were conducted at multiple spatial resolutions. The results demonstrate that GeoClimaProx outperforms traditional feature combinations, contributing to improved accuracy and spatial resolution in heat flow prediction, and TabPFN exhibits superior generalization ability and higher predictive accuracy across various spatial resolutions and training data sizes. GeoClimaProx and TabPFN provide a novel technical pathway for accurate heat flow prediction using only easily accessible surface observations and tectonic distance features. Based on them, the first 0.2° resolution heat flow map covering global continental regions is constructed, improving the spatial resolution of heat flow modeling, which means the beginning of fine-scale modeling of continental heat flow at the global scale. The code, datasets, and resulting data products are publicly available at https://github.com/zhang152267/GCHF to facilitate reproducibility.