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The carbon isotope composition (δ¹³C_Leaf) of C₃ plant leaves provides valuable information on the carbon-water cycle of vegetation and their responses to climate change within terrestrial ecosystems. However, global applications of δ¹³C_Leaf are hindered by a lack of global long-term spatial maps (isoscapes) that capture vegetation δ¹³C_Leaf variations. The ways in which δ¹³C_Leaf varies over time and across regions are still unknown. In this study, we collected leaf carbon isotope samples across the globe and selected the optimal predictive model from three machine learning algorithms to construct long-term annual global δ¹³C_Leaf isoscapes at a spatial resolution of 0.05° for natural C₃ plants between 2001 and 2020. We also assessed the potential of remotely sensed spectral bands, atmospheric CO₂ characteristics, geographic, and physiological information to estimate the δ¹³C_Leaf of the global C₃ …