Biotic invasions threaten global biodiversity and ecosystem function. Such incursions present challenges to agriculture where invasive pest species cause significant production losses require major economic investment to control and can cause significant production losses. Pest Risk Analysis (PRA) is key to prioritizing agricultural biosecurity efforts, but is hampered by incomplete knowledge of current crop pest and pathogen distributions. Here we develop predictive models of current pest distributions and test these models using new observations at sub-national resolution. We apply generalized linear models (GLM) to estimate presence probabilities for 1739 crop pests in the CABI pest distribution database. We test model predictions for 100 unobserved pest occurrences in the People’s Republic of China (PRC), against observations of these pests abstracted from the Chinese literature. This resource has hitherto been omitted from databases on global pest distributions. Finally, we predict occurrences of all unobserved pests globally. Presence probability increases with host presence, presence in neighbouring regions, per capita GDP, and global prevalence. Presence probability decreases with mean distance from coast and known host number per pest. The models were good predictors of pest presence in Provinces of the PRC, with area under the ROC curve (AUC) values of 0.75 – 0.76. Large numbers of currently unobserved, but probably present pests (defined here as unreported pests with a predicted presence probability > 0.75), are predicted in China, India, southern Brazil and some countries of the former USSR. Our results shows that GLMs can predict presences of pseudo-absent pests at sub-national resolution. The Chinese scientific literature has been largely inaccessible to Western academia but contains important information that can support PRA. Prior studies have often assumed that unreported pests in a global distribution database represents a true absence. Our analysis provides a method for quantifying pseudo-absences to enable improved PRA and species distribution modelling.