A final summary figure summarizing the results of a study that compares two different downscaling techniques in terms of how they project future change in various aspects of the maple syrup tapping season. Spatially, the larger project uses raster data covering the full range of sugar maple across the northeastern US, but the summary figure represents point data (as an example) at a sugarbush farm in central Wisconsin. The results show that dynamically downscaled models fail to adequately forecast absolute values of future conditions but do capture potential changes in year-to-year variability — a metric of particular concern to producers as it challenges planning. Statistically downscaled models, while they do adequately capture the absolute range in future conditions (i.e., produce reasonable values, i.e., all positive, as would be expected in a warming climate), they dampen the effects on year-to-year variability. In summary, both downscaling methods have strengths and weaknesses, and both provide useful information to producers. Therefore, complete information requires both downscaling types.