Abstract: Evaluating climate and vegetation status in earth system models (ESMs) is fundamental to understanding climate change, terrestrial ecosystems, and the carbon cycle. Our study examines temperature, precipitation, and leaf area index (LAI) during the growing season in China, using data from eighteen ESMs part of the Sixth International Coupled Model Comparison Project (CMIP6). This analysis was underpinned by site observations and remote sensing data. We employed a multiple linear regression model to quantify the sensitivity of LAI to temperature and precipitation, aiming to evaluate the ability of the CMIP6 model to simulate the sensitivity of vegetation in geographical and climatic spaces. Ultimately, models demonstrating superior simulation performance were selected. Our results show that: (1) While most models can simulate the spatial distribution of temperature, precipitation, and LAI during the growing season, significant discrepancies are evident in their mean values and trend patterns. (2) Compared to observations, the simulation ability of LAI sensitivity to temperature and precipitation was more accurate for regions exhibiting positive sensitivity. It was observed that the sensitivity of vegetation in ecotone was greater than elsewhere in China. However, the magnitude and distribution of vegetation sensitivity across climate spaces showed considerable variance (i.e., the corresponding relationship with the climate field). (3) After extensive evaluations, CANESM5–CanOE, INM–CM5–0, IPSL–CM6–LR, and MPI–ESM1–2–LR demonstrated the best performance on simulations of vegetation sensitivity to climate during China’s growing season.