Many species occurrence records from specimens and publications are based on regions such as administrative units. These region-based records are accessible and dependable, and sometimes they are the only available data source; however, few species distribution models accept such data as direct input. In this paper, we present a method named Mikrubi for robust prediction of species distributions from region-based occurrence data and a Julia package implementing the algorithms. The package ‘Mikrubi' requires a map describing disjoint regions, climatic raster layers, and a list of occupied regions. Mikrubi then rasterizes the regions, reduces the environmental dimensionality, parameterizes the niche, and finally estimates the parameters by maximizing the likelihood. In a simulation study, we find Mikrubi effective in accurate estimation in most cases; in a case study of Allium wallichii in China, Mikrubi significantly outperforms four modeling strategies that adapt region-based records to conventional models according to different principles. The package has many prospective applications in addition to modeling distributions on region-based records: 1) it accepts supplementary coordinates; 2) it is a new solution for distribution modeling using deviated coordinates; and 3) its probabilistic region-based outputs have special uses in conservation and biodiversity science.