The article presents the CHELSA (Climatologies at high resolution for the earth’s land surface areas) dataset, which provides high-resolution (30 arc sec) temperature and precipitation estimates derived from the ERA-Interim climatic reanalysis. The temperature algorithm is based on statistical downscaling, while the precipitation algorithm incorporates orographic predictors such as wind fields, valley exposition, and boundary layer height, followed by bias correction. The resulting data cover the period from 1979 to 2013 and are validated against other gridded products and station data from the Global Historical Climate Network. The CHELSA climatologies are shown to improve the accuracy of species range predictions in species distribution modeling, particularly for precipitation patterns. The methods used to calculate monthly temperature and precipitation values, including the correction for orographic effects, are detailed. The validation results demonstrate that CHELSA performs better than other datasets in capturing small-scale precipitation patterns, especially in complex terrain. The article also includes a comparison of CHELSA with other datasets and a validation of temperature data using independent meteorological stations. Finally, an application example in species distribution modeling highlights the improved performance of CHELSA over WorldClim.The article presents the CHELSA (Climatologies at high resolution for the earth’s land surface areas) dataset, which provides high-resolution (30 arc sec) temperature and precipitation estimates derived from the ERA-Interim climatic reanalysis. The temperature algorithm is based on statistical downscaling, while the precipitation algorithm incorporates orographic predictors such as wind fields, valley exposition, and boundary layer height, followed by bias correction. The resulting data cover the period from 1979 to 2013 and are validated against other gridded products and station data from the Global Historical Climate Network. The CHELSA climatologies are shown to improve the accuracy of species range predictions in species distribution modeling, particularly for precipitation patterns. The methods used to calculate monthly temperature and precipitation values, including the correction for orographic effects, are detailed. The validation results demonstrate that CHELSA performs better than other datasets in capturing small-scale precipitation patterns, especially in complex terrain. The article also includes a comparison of CHELSA with other datasets and a validation of temperature data using independent meteorological stations. Finally, an application example in species distribution modeling highlights the improved performance of CHELSA over WorldClim.