The U.S. Northeast Shelf (NES) is a dynamic and economically important marine ecosystem where temperature and salinity variability are shaped by interactions among large-scale climate variability, Gulf Stream shifts, mesoscale eddies, and local shelf processes. Predicting these variations on multi-year timescales remains a major challenge for current climate systems, as global models at typically 1–2° resolution exhibit poor skills for the NES. Here, we evaluate a high-resolution regional prediction of NES based on the downscaling of global Community Earth System Model Decadal Prediction Large Ensemble (CESM-DPLE) using the Regional Ocean Modeling System (ROMS-DOWN) to assess its potential for improving interannual-to-decadal prediction skill on the NES. Compared to CESM-DPLE, ROMS-DOWN substantially reduces mean-state biases in temperature, salinity, sea surface height, and upper-ocean heat content across the shelf and slope, where bathymetry effect and shelf-slope exchange are critical but poorly resolved in global models. Both deterministic and probabilistic metrics indicate improved forecast performance with lead time up to 5 years. The predictive skill reflects contributions from externally forced trends and interannual-to-decadal internal variability, with dominant timescales of predictability differing among variables. ROMS-DOWN also skillfully reproduces key shelf features such as the Middle Atlantic Bight cold pool and slope-water mixing characteristics in the Gulf of Maine, though their predictability remains moderate likely due to internal variability, boundary condition biases, and model uncertainties. Overall, these results demonstrate that dynamical downscaling can effectively bridge large-scale climate predictability and regional coastal processes, providing a foundation for improved multi-year prediction and understanding of ocean variability on the NES.