Bioclimatic variables are indicators reflecting the integrated relationship between living things and climate. They are often used to interprete the relationships between species, vegetations and climate in global change research, and further simulate the geographical distribution patterns of both species and vegetations, as well as their functional characteristics. Regional bioclimate datasets, however, have been rarely reported. Based on an ANUSPIN interpolated dataset (covering temperature, precipitation and sunshine percentage) of 1km-resolution climate variables in China at 30-year basis averaged from 1951 to 1980 and from 1981 to 2010, respectively, we calculated 9 kinds bioclimatic variables in this study, namely mean temperature of the coldest month, mean temperature of the warmest month, absolute maximum temperature, absolute minimum temperature, annual growing degree days above 0°C and 5°C, growing season precipitation, annual drought index and annual moisture index. We plotted their spatial distribution map and analyzed their spatial pattern and trend statistically. Comparative analysis shows that the variation range of corresponding variables is very narrow, and the statistical variables are nearly the same. Therefore, the error of this dataset mainly comes from the spatial distribution dataset of basic climatic factors, and the secondary error in the process is tiny.This dataset provides reasonable environmentally mechanistic explanations for research on the relationships between species, vegetations and climate, and offers a convenient and diverse way for researchers to use bioclimatic variables to simulate species distribution patterns, vegetation structures and functions.

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The areas with karst topography in Southwest China have a fragile ecological environment and the ecosystem there is vulnerable to climate change and human activities. Due to the influence of the karst topography, the spatial distribution of weather stations in this area is uneven which, together with the slight difference of meteorological observation time series of each observation station and the limited number of stations, makes it difficult for the observed data to be used in the study on the realationship between terrestrial ecosystems and climate change. In this study, we used the local smooth thin plate spline function from the ANUSPLIN software version 4.3, combining with the Shuttle Radar Topography Mission (SRTM) digital elevation model, to spatially interpolate four monthly climatic variables (i.e. temperature, precipitation, sunshine percentage, and wet days with daily precipitation <0.1 mm). In this way, we finally obtained three sets of gridded data in different formats with a resolution of 1km. The error statistics show that the error of the interpolation results is relatively low, especially with a high accuracy of the temperature interpolation. The gridded data of the four climate variables can truly reflect the spatial distribution of climates in the karst areas. Further analyses show that from 1951 to 2014, the distribution of temperature and precipitation showed a decreasing trend from the southeast to the northwest. The overall change of temperature showed an upward trend, and the change trend of precipitation was not significant. The distribution of sunshine percentage gradually decreased from the middle to the two sides, and the sunshine percentage showed an overall decline trend. The distribution patterns of wet days are inversely related to altitudes. This dataset can provide data support for the regional research on climate, the relationship between vegetation, rocky desertification and climate change, the relationship between land use and land cover changes, as well as the climate–driven terrestrial ecological model simulations.