Leaf area index (LAI) and fractional vegetation cover are widely used to characterize vegetation in land models for land-atmosphere interaction studies. The prediction of LAI is one of the core tasks of Dynamic Vegetation Models (DVMs), and the spatiotemporal relationship between the climate and simulated LAI or other vegetation variables simulated by DVMs needs to be evaluated and better understood. In this work, the Dynamic Global Vegetation Model in the Community Land Model version 3.0 (CLM3.0-DGVM) is utilized to address this issue by evaluating the simulated LAI using the new plant function type (PFT) LAI parameters derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) data developed by The National Center for Atmospheric Research. The Moran's autocorrelation index is used to determine the degree of clustering of the simulated annual LAI maximum (LAIx) in the absence of ancient agricultural practices and modern industrialization. Then the stepwise regression algorithm is applied to construct an optimal multivariate linear regression equation for each PFT between LAIx (dependent variable) and climatic factors (independent variables). In this way the dominant and secondary factors as well as their statistical significance can be quantified. Furthermore, the temporal relationship between LAIx and five climatic factors for each PFT at interannual time scales under current climatological condition is investigated using time-lagged correlation analysis. The conclusions from these model-data analyses are: (1) the modified CLM3.0-DGVM is able to simulate well the mean LAIx value for each PFT and reproduce the global biogeographic patterns of LAI, but it still has deficiencies in the simulation of PFT phenology (i.e. LAI seasonal cycle); (2) there is a positive spatial autocorrelation of LAIx within each PFT. The spatial distribution pattern of LAIx is strongly influenced by the climatic factors, and this effect differs for different PFTs. Generally, solar radiation and precipitation are the first-order impact factors, followed by specific humidity; (3) the interannual trends of LAIx simulated by CLM3.0-DGVM has a significant 1-year or 2-year lag relationship with some climatic factors, mainly because LAIx is calculated from previous year's net primary production in this model. Among the five climatic factors, solar radiation and precipitation have larger correlations with the LAIx in the subsequent one or two years than temperature and specific humidity, while the wind has a negligible correlation with LAIx. The implications of these characteristics of LAI and LAI-climate relations revealed in this DGVM for the general vegetation-climate interactions in nature are: different climatic factors have different effects on different plants in different regions, the biogeographic pattern of vegetation is a composite of individualistic responses to climatic factors of different plants which implies that terrestrial ecosystem exhibits complex behavior at different spatiotemporal scales; and for each PFT, it is crucial to identify the dominant climatic factor and understand the main biophysical and biogeochemical processes. Work is still needed to further evaluate the DGVM using more accurate and higher-resolution climatic data and in-situ LAI data. In addition, the DGVM needs to be further improved (e.g., by including crops and irrigation).