The climate and hydrology of the Great Lakes region is controlled by the mix of cold-dry Arctic air, warm-dry air from the Pacific, and warm-wet air from the Gulf of Mexico. Future changes in any of these systems or in their interaction could lead to profound changes in the water fluxes to, from, and within the basin. Whereas global-scale hydrological cycles are characterized by a robust response in which the dry regions get drier and the wet regions get wetter, the Great Lakes Basin lies near the boundary separating these large scale regions of change and consequently the predicted hydrological balance (Evaporation minus Precipitation) could shift in either direction. To address how the regional hydrological balance may be affected by climate change, projections from global climate models must be downscaled to the scales relevant to hydrological modeling; and the choice of downscaling method can be just as important as the original choice of which global climate model and which climate scenarios to use. This project quantifies changes in projections of streamflow metrics between three very different commonly-used downscaling methods of varying complexity: a delta approach, a quantile mapping method (BCSD) that has been used in many hydrological studies across North America, and a new asynchronous regional regression approach (ARRM), that resolves potential changes in the daily distribution of temperature and precipitation as simulated by the global models by developing a time-independent relationship between ranked observations and historical simulations. Metrics were selected to evaluate the projected changes to ecologically significant high-flows, low-flows and daily variability as well as flood frequency analysis resulting from each downscaling method. Hydrologic simulations were conducted for the four states surrounding Lake Michigan, Wisconsin, Illinois, Indiana and Michigan, with streamflow routed for major rivers. Rescaled reanalysis wind fields are also introduced and factor separation is used to evaluate the significance of different climate fields on streamflow metrics.