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Species distribution modeling is playing an increasingly prominent role in ecology and global change biology, owing to its potential to predict species range shifts, biodiversity losses, and biological invasion risks for future climates. Such models are now well-established as important tools for biological conservation. However, the lack of high-resolution data for future climate scenarios has seriously limited their application, particularly because of the scale gap between general circulation models (GCMs) and species distribution models (SDMs). A recently introduced change-factor downscaling technique provides a convenient way to build high-resolution datasets from GCM projections. Here, we present a high-resolution (10’ × 10’) global bioclimatic dataset (BioPlant) for plant species distribution. The 15 bioclimatic variables we select are considered those most eco-physiologically relevant. They can be easily calculated from climatic variables common to all GCM projections. In addition to the traditional classes of variables regarding temperature and precipitation, the BioPlant dataset emphasizes the interactions between temperature and precipitation, particularly within plant growing seasons. A preliminary visual analysis shows that variations among GCMs are more significant on a species range scale than on a global scale. Thus, the formerly advocated ensemble modeling method should be applied not only to different SDMs, but also to various GCMs. Statistic analysis suggests that divergent behavior among GCM variations for temperature class variables and classes of precipitation variables requires special attention. Our dataset may provide a common platform for ensemble modeling, and can serve as an example to develop higher-resolution regional datasets.