Hydroclimate and terrestrial hydrology greatly influence the local community, ecosystem, and economy in Alaska and Yukon River Basin. A high-resolution re-simulation of the historical climate in Alaska can provide an important benchmark for climate change studies. In this study, we utilized the Regional Arctic Systems Model (RASM) and conducted coupled land-atmosphere modeling for Alaska and Yukon River Basin at 4-km grid spacing. In RASM, the land model was replaced with the Community Terrestrial Systems Model (CTSM) given its comprehensive process representations for cold regions. The microphysics schemes in the Weather Research and Forecast (WRF) atmospheric model were manually tuned for optimal model performance. This study aims to maintain good model performance for both hydroclimate and terrestrial hydrology, especially streamflow, which was rarely a priority in coupled models. Therefore, we implemented a strategy of iterative testing and re-optimization of CTSM. A multi-decadal climate dataset (1990-2021) was generated using RASM with optimized land parameters and manually tuned WRF microphysics. When evaluated against multiple observational datasets, this dataset well captures the climate statistics and spatial distributions for five key weather variables and hydrologic fluxes, including precipitation, air temperature, snow fraction, evaporation-to-precipitation ratios, and streamflow. The simulated precipitation shows wet bias during the spring season and simulated air temperatures exhibit dampened seasonality with warm biases in winter and cold biases in summer. We used transfer entropy to investigate the discrepancy in connectivity of hydrologic fluxes between the offline CTSM and coupled models, which contributed to their discrepancy in streamflow simulations.