Climate change is highly likely to impact on forest productivity over the next century. The direction and magnitude of change are uncertain because many factors are changing simultaneously, such as atmospheric composition, temperature, rainfall, and land use. Simulation models have been widely used to estimate how these interacting factors might combine to alter forest productivity. Such studies have used many different types of models with different underlying assumptions. To evaluate predictions made by such studies, it is essential to understand the type of model and the assumptions used. In this article, we provide a checklist for use when evaluating modeled estimates of climate change impacts on forest productivity. The checklist highlights the assumptions that we believe are critical in determining model outcomes. Models are classified into different general types, and assumptions relating to effects of atmospheric CO2 concentration, temperature, water availability, nutrient cycling, and disturbance are discussed. Our main aim is to provide a guide to enable correct interpretation of model projections. The article also challenges modelers to improve the quality of information provided about their model assumptions.