Geographic complexity - the explicit integration of complexity research with space and place-based research - faces interrelated methodological, conceptual, and policy challenges. The rubric of model evaluation is central both to understanding and to meeting these challenges. They include methodological issues such as sensitivity and complex scaling; the conceptual challenges of conflating pattern and process, and reconciling simplicity and complexity; and policy issues posed by the science-policy gap and postnormal science. The importance of these challenges and the centrality of model evaluation in meeting them are demonstrated through examples drawn from human-environment systems, with particular reference to global environmental change and land-use and land-cover change. Specific model-evaluation strategies are also offered.