[Objective] The dynamic variation of vegetation and its corresponding driving mechanism (i.e., climate change) were explored to provide a basis for understanding how the regional and global dynamic variations of vegetation respond to climate change.[Methods] The moderate-resolution imaging spectroradiometer (MODIS) normalized-difference vegetation index (NDVI) time series along with digital elevation model (DEM) data and monthly precipitation/temperature data were used for a Theil-Sen Median analysis, Mann-Kendall significance test, R/S analysis, Pearson correlation analysis, and ANUSPLIN model. The spatiotemporal variation of vegetation and its future trend were analyzed, and the relation of the NDVI with precipitation and temperature was explored. Furthermore, the time-lag effects of the variation of vegetation in response to climate variables in the Beijing-Tianjin-Hebei region from 2001 to 2019 were investigated.[Results] ① During the period from 2001 to 2019, the vegetation cover increased in Beijing-Tianjin-Hebei region at a rate of 0.002 2/a. The area that is expected to degrade in the future exceeded that of the area that was expected to improve. ② Furthermore, both precipitation and temperature exerted positive effects on vegetation growth in most areas, although the response of vegetation growth to changes in precipitation was more pronounced than that of temperature. ③ The lag time for the maximum response of vegetation growth to precipitation was longer than that of temperature. More specifically, the maximum vegetation cover was associated with the precipitation of the previous 3 months and the temperature of the previous one month.[Conclusion] Due to the implementation of ecological restoration projects, the vegetation cover improved significantly in the Beijing-Tianjin-and Hebei region from 2001 to 2019, especially in the northwestern area. The relation of the NDVI with precipitation and temperature exhibited regional characteristics, and vegetation growth showed obvious lag-time effects to changes in climate variables.