In Arabian Sea (AS), landlocked northern boundary and strong seasonal productivity lead to the formation of one of the most intense open ocean Oxygen Minimum Zones (OMZs). Presence of this perennial OMZ has significant consequences on adjacent coastal fisheries and ecosystem. Simulations from CMIP5 suggest significant weakening of both monsoonal winds and productivity under high emission scenario. But the fate of AS OMZ in this scenario-whether it will expand or shrink-still remains elusive, mainly due to poor representation of extent and strength of AS OMZ in CMIP5 presentday simulations. To address this, we analyze the distribution of O 2 in AS from a subset of three contrasted CMIP5 simulations, and complemented with a set of regional downscaled model experiments which we forced at surface and open boundaries using information from those three CMIP5 models. We tested two regional downscaling approaches-with and without correction of CMIP5 biases with respect to observations. Using a set of sensitivity experiments, we disentangle the contributions of local (atmospheric) forcing vs. remote (at the lateral boundaries) forcing in driving the future projected O 2 changes. While CMIP5 projects either shrinking or expansion of the AS OMZ depending on the model, our downscaling experiments consistently project a shrinking of AS OMZ. We show that projected O 2 changes in OMZ layer are affected by both local and remote processes. In the southern AS, the main response to climate change is oxygenation that originates from the boundaries, and hence downscalled and CMIP5 model responses are similar. In contrast, in northern AS, downscaling yields a substantial reduction in O 2 projection discrepancies because of a minimal influence of remote forcing there leading to a stronger sensitivity to improved local physics and improved model representation of present-day conditions. We find that when corrected for present-day biases, projected deoxygenation in the northern AS is shallower. Our findings indicate the importance of downscaling of global models in regions where local forcing is dominant, and the need for correcting global model biases with respect to observations to reduce uncertainties in future O 2 projections.