Models transferred to novel conditions could provide predictions in data-poor scenarios, contributing to more informed management decisions. The determinants of ecological predictability are, however, still insufficiently understood. Predictions from transferred ecological models are affected by species’ traits, sampling biases, biotic interactions, nonstationarity, and the degree of environmental dissimilarity between reference and target systems. We synthesize six technical and six fundamental challenges that, if resolved, will catalyze practical and conceptual advances in model transfers. We propose that the most immediate obstacle to improving understanding lies in the absence of a widely applicable set of metrics for assessing transferability, and that encouraging the development of models grounded in well-established mechanisms offers the most immediate way of improving transferability.