Water use efficiency (WUE) serves as a crucial metric for terrestrial carbon-water coupling, yet systematic gaps persist in understanding the spatial patterns and drivers of leaf-level intrinsic WUE (iWUE) versus ecosystemscale WUE (WUEEco). Combining machine learning with 1,446 leaf delta 13Cp records, we investigated the spatial heterogeneity and main drivers of iWUE and WUEEco across different life forms and climate zones in China. Results showed that inverse spatial patterns, where iWUE peaked in arid northwestern grasslands (60.46 mu mol mol- 1). In contrast, WUEEco exhibited maxima in humid southeastern forests (1.82 g C/kg H2O). Hierarchical partitioning and structural equation modeling revealed that elevation indirectly influenced iWUE (17.72 %) and WUEEco (25.64 %) through its modification of climatic conditions. Vegetation factors (e.g., leaf area index) and climatic factors (e.g., relative humidity) emerged as key drivers of iWUE (24.06 %) and WUEEco (15.31 %), primarily through their regulation of photosynthesis-transpiration coupling processes. Among four machine learning models, Random Forest has the best performance in iWUE prediction (R2 = 0.73, NRMSE = 0.122, MBE = - 0.078), providing a high-resolution national iWUE dataset. This study highlights the importance of scale in understanding carbon-water interactions and provides a valuable reference for water resource management under climate change.