Abstract:[Objective] In order to accurately estimate net primary productivity (NPP) of vegetation under the context of rising atmospheric CO2 concentrations, [Methods] a CO2 concentration factor was introduced into the Carnegie-Ames-Stanford Approach (CASA) model to simulate the maximum light use efficiency of different vegetation types. Based on this, the potential of the improved model for NPP estimation was explored. [Results] From 2000 to 2020, the annual average atmospheric CO2 concentration in China's terrestrial areas showed a significant increasing trend, with an average increase of approximately 2.14 μmol·mol?1·a?1. After incorporating the CO2 stress factor, the vegetation types were ranked by maximum light use efficiency in the following order: cropland > evergreen broadleaf forest > mixed forest > needleleaf forest > shrub > wetland > grassland (1.85, 1.69, 1.30, 0.87, 0.52, 0.40, 0.40 gC·MJ?1). The estimation accuracy of NPP by the CASA_CO2 model was improved compared to the original CASA model. The R2 between the simulated and measured NPP values at the vegetation level and the comprehensive level increased by -0.1% to 7.2% and 0.5%, respectively, and RMSE decreased by 0.3% to 9.2% and 0.7%, respectively. The RMSE of annual NPP estimation results decreased by 1.9%. The CASA_CO2 model improved the underestimation of overall NPP by the CASA model, reducing it from 10.62% to 9.81%. The CASA_CO2 model underestimated the overall NPP for spring, autumn, and winter by 5.11%, 2.72%, and 2.51%, respectively, while overestimating it for summer by 0.53%. [Conclusion] Considering the impact of CO2 concentration changes on vegetation in the model can improve the accuracy of NPP estimation.