Plants assimilate carbon through photosynthesis (gross primary productivity, GPP) while losing water via transpiration (Trans), with both processes responding nonlinearly to temperature. Although the air temperature optimum of GPP ( \({T}_{\mathrm{opt}}^{\mathrm{GPP}}\) ) is well studied, the thermal response of Trans ( \({T}_{\mathrm{opt}}^{\mathrm{Trans}}\) ) remains unknown. Here, using global eddy covariance observations and sap flow measurements along with simulations from an Earth system model, we find that \({T}_{\mathrm{opt}}^{\mathrm{Trans}}\) is consistently higher than \({T}_{\mathrm{opt}}^{\mathrm{GPP}}\) across biomes and climates, indicating greater heat tolerance in Trans. Despite a strong correlation, their divergence suggests carbon uptake is more vulnerable to warming than water loss. Machine learning identifies maximum air temperature as the key driver of both optima, while their difference ( \({\Delta T}_{\mathrm{opt}}\) ) is associated with vegetation water content. The Earth system model predicts spatial patterns of \({T}_{\mathrm{opt}}^{\mathrm{Trans}}\) and \({T}_{\mathrm{opt}}^{\mathrm{GPP}}\) that align with observations, but the model significantly underestimates the magnitudes of \({T}_{\mathrm{opt}}^{\,\mathrm{Trans}}\) , \({T}_{\mathrm{opt}}^{\mathrm{GPP}}\) and \({\Delta T}_{\mathrm{opt}}\) . These results reveal a critical decoupling of carbon–water coordination under heat stress, with ecosystems sustaining Trans beyond \({T}_{\mathrm{opt}}^{\mathrm{GPP}}\) to cool leaves, but ultimately reducing Trans to conserve water.
