Predicting the probability of successful establishment and invasion of alien species at global scale, by matching climatic and land use data, is a priority for the risk assessment. Both large- and local-scale factors contribute to the outcome of invasions, and should be integrated to improve the predictions. At global scale, we used climatic and land use layers to evaluate the habitat suitability for the American bullfrog Rana catesbeiana, a major invasive species that is among the causes of amphibian decline. Environmental models were built by using Maxent, a machine learning method. Then, we integrated global data with information on richness of native communities and hunting pressure collected at the local scale. Global-scale data allowed us to delineate the areas with the highest suitability for this species. Predicted suitability was significantly related to the invasiveness observed for bullfrog populations historically introduced in Europe, but did not explain a large portion of variability in invasion success. The integration of data at the global and local scales greatly improved the performance of models, and explained > 57% of the variance in introduction success: bullfrogs were more invasive in areas with high suitability and low hunting pressure over frogs. Our study identified the climatic factors entailing the risk of invasion by bullfrogs, and stresses the importance of the integration of biotic and abiotic data collected at different spatial scales, to evaluate the areas where monitoring and management efforts need to be focused.