Evaluation of climate and vegetation status in earth system models (ESMs) is fundamental to understanding climate change, terrestrial ecosystems, and the carbon cycle. In this study, the temperature, precipitation, and LAI in the growing season over China from eighteen ESMs of the Sixth International Coupled Model Comparison Project (CMIP6) were evaluated based on site observation and remote sensing data. Then, a multiple linear regression model was used to quantify the sensitivity of LAI to temperature and precipitation, and to evaluate the ability of the CMIP6 model to simulate the sensitivity of vegetation in geographical and climatic spaces. At last, the models with a better simulation performance were selected. The results show that (1) Most models can simulate the spatial distribution of temperature, precipitation, and LAI in the growing season, but there are obvious deviations in their mean value and change trends. (2) Compared with the observation, The simulation ability of LAI sensitivity to temperature and precipitation showed that the simulation of the positive region was better than the negative region, and the sensitivity of vegetation in ecotone was greater than that in China. There was a large deviation in the amplitude of vegetation sensitivity and its distribution in climate space (i.e., the corresponding relationship with climate field). (3) Comprehensively based on evaluations above, CANESM5-CanOE, INM-CM5-0, IPSL-CM6-LR, and MPI-ESM1-2-LR have the best performance on simulations of climate and vegetation during the growing season in China.