It is revealed through principal component and factor analysis methods that there exist four types of seasonal patters of spatial differentiation of land corer change in China. The relationship between NDVI and seasonal and interannual precipitation and temperature change were studied for 17 typically vegetation types in China. The results show that impacts of precipitation and temperature interannual change on vegetation growth are different for regions of different vegetation types. The data of temperature and precipitation are used to predict NDVI in selected areas in China by methods of artificial neural networks and stepwise regression. The result shows that combining the two methods is helpful in improving prediction accuracy. Land cover change and climate change interaction through biophysical processes that involve the transfers of energy and water at the land surface and biophysical cycles that affect the concentrations of greenhouse gases and aerosols in the atmosphere. The impact of land cover change on the mesoscale boundary layer structure of the midlatitude semiarid area has been investigated by using a mesoscase biological meteorological model coupled with a cumulus ensemble parameterization scheme. A new China climate-vegetation model is developed, in which soil is taken as a limiting factor and elevation as an affecting factor. As a result, not only is the total precision of the model improved, but also is the precision of each vegetation type amended significantly.