State-of-the-art vegetation models overestimate gross primary productivity responses to drought

Climate change has a substantial impact on ecosystem gross primary productivity (GPP), but the specific roles of different climatic drivers across various vegetation types remain unclear. This study investigated GPP responses to variations in temperature, precipitation, and drought using simulations from 17 dynamic vegetation models and compared them with two benchmark GPP datasets. The models reasonably capture the positive temperature sensitivity of GPP in boreal regions (0.01 to 0.06 g C m 2 d −1 K −1 ) and the negative responses in the tropics, such as in evergreen broadleaf forests (−0.08 ± 0.19 g C m 2 d −1 K −1 ). The simulations also reproduce the observed weak positive precipitation sensitivity in forests and strong positive responses in non-tree vegetation. However, they systematically overestimate such an effect of precipitation, leading to higher predicted drought sensitivity in tropical grasslands and lower resilience in trees. Both benchmark datasets and model simulations exhibited negative GPP responses to extreme drought on a global scale, though models tended to overestimate the magnitude of these negative effects. Overall, this study distinguished GPP responses to key climatic factors across vegetation types and model structures, providing critical insights for improving predictions of terrestrial carbon sinks and promoting the climatic resilience of ecosystems.