Abstract: Based on the global land cover type and coverage, we used the NDVI (Normalized Difference Vegetation Index) and averaged climate state data (temperature, precipitation) of the growing season from 1982 to 2015 in this study. The relationship between global vegetation distribution and climate factors was discussed, and a multiple regression model was developed. The sensitivity of vegetation to climate states (temperature and precipitation) was analyzed. There was an apparent correspondence between vegetation and climate factors on the climate gradient. The regression model has fitted the distribution pattern of climatic NDVI well, and the correlation coefficient between global fitting and the observed NDVI was 0.90. Among them, the fitting ability of spatial distribution of the broadleaf evergreen forests, mixed forests, needleleaf evergreen forests, broadleaf deciduous forests, and cropland and woody savanna were great (r＞0.8). The NDVIs of different land cover types demonstrated different spatial sensitivity characteristics to temperature and precipitation climate states. Overall, the sensitivity of vegetation to temperature and precipitation demonstrated an inversed correlation (r=−0.6). Different land cover types showed positive/negative sensitivity to temperature. Boreal shrubs demonstrated the greatest sensitivity to temperature, while crops, grasslands, and bare land proved the high negative sensitivity to temperature than others. The sensitivity of vegetation to precipitation was positive, and the spatial sensitivity of needleleaf deciduous forests, grass, and savanna to precipitation was high.