Dataset for <b>Global distribution and drivers of </b><b>leaf maximum carboxylation rate</b>

The maximum carboxylation rate of Rubisco (V_cmax) is a crucial parameter in terrestrial biosphere models (TBMs) for simulating photosynthetic carbon assimilation. Most TBMs, however, prescribe V_cmaxas a constant within each plant functional type, neglecting spatial variability and limiting the accuracy of terrestrial carbon cycle estimates. Although several attempts have been made to map V_cmax globally, its large-scale patterns and controlling factors remain poorly understood. Here, we compiled 6386 in situ measurements of V_cmax together with leaf nitrogen (LNC), phosphorus (LPC), and mass per area (LMA), and used machine learning to generate a global V_cmax map and identify its drivers. The predicted V_cmax shows strong agreement with three existing products (R² ≥ 0.74) and have the best performance when validated against independent field data. We further find that leaf nutrients, climate, and soil jointly regulate global V_cmax variation, with LNC, LPC, and soil pH emerging as primary predictors. These results advance understanding of the factors controlling V_cmax, and suggest that projections of photosynthetic capacity and terrestrial carbon fluxes could be markedly improved by incorporating the interactive effects of leaf nutrient traits, climate, and soil properties into V_cmax parameterization in TBMs.