The Arctic hydrological system is an interconnected system that is experiencing rapid change. It is comprised of permafrost, snow, glacier, frozen soils, and inland river systems. Permafrost degradation, trends towards earlier snow melt, a lengthening snow-free season, soil ice melt, and warming frozen soils all challenge hydrologic simulation under climate change in the Arctic. In this study, we provide an improved representation of the hydrologic cycle across a regional Arctic domain using a generalizable optimization methodology and workflow for the community. We applied the Community Terrestrial Systems Model (CTSM) across the US state of Alaska and the Yukon River Basin at 4-km spatial resolution. We highlight several potentially useful high-resolution CTSM configuration changes. Additionally, we performed a multi-objective optimization using snow and river flow metrics within an adaptive surrogate-based model optimization scheme. Four representative river basins across our study domain were selected for optimization based on observed streamflow and snow water equivalent observations at ten SNOTEL sites. Fourteen sensitive parameters were identified for optimization with half of them not directly related to hydrology or snow processes. Across fifteen out-of-sample river basins, thirteen had improved flow simulations after optimization and the median Kling-Gupta Efficiency of daily flow increased from 0.40 to 0.63. In addition, we adapted the Shapley Decomposition to disentangle each parameter’s contribution to streamflow performance changes, with the seven non-hydrological parameters providing a non-negligible contribution to performance gains. The snow simulation had limited improvement, likely because snow simulation is influenced more by meteorological forcing than model parameter choices.