Downscaling is the process of making a coarse-scale global climate model into a finer resolution in order to capture some of the localized detail that the coarse global models cannot resolve. There are two general approaches of downscaling: dynamical and statistical. Within those, many dynamical models have been developed by different institutions, and there are a number of statistical algorithms that have been developed over the years. Many past studies have evaluated the performance of these two broad approaches of downscaling with respect to climate variables (e.g., temperature, precipitation), but few have translated these evaluations to ecological metrics that managers use to make decisions in planning for climate change. This study used maple syrup production as a case study for evaluating how these two downscaling techniques perform in terms of projecting changes in the tapping season. Maple syrup producers are interested in how climate change will affect their industry. Our results offer them a sense of the range they can expect for different aspects of the tapping season (timing of tap date, length of season, quality of sap, etc.). However, producers are a secondary audience for this study. The primary audience is the greater ecology community, as this demonstrates the importance of careful selection of different climate modeling products and a sense of the limitations of different products.
