Water resources planning often uses streamflow predictions made by hydrologic models. These simulated predictions have systematic errors which limit their usefulness as input to water management models. To account for these errors, streamflow predictions are bias-corrected through statistical methods which adjust model predictions based on comparisons to reference datasets (such as observed streamflow). Existing bias-correction methods have several shortcomings when used to correct spatially-distributed streamflow predictions. First, existing bias-correction methods destroy the spatio-temporal consistency of the streamflow predictions, when these methods are applied independently at multiple sites across a river network. Second, bias-correction techniques are usually built on simple, time-invariant mappings between reference and simulated streamflow without accounting for the hydrologic processes which underpin the systematic errors. We describe improved bias-correction techniques which account for the river network topology and which allow for corrections that are process-conditioned. Further, we present a workflow that allows the user to select whether to apply these techniques separately or in conjunction. We evaluate four different bias-correction methods implemented with our workflow in the Yakima River Basin in the Pacific Northwestern United States. We find that all four methods reduce systematic bias in the simulated streamflow. The spatially-consistent bias-correction methods produce spatially-distributed streamflow as well as bias-corrected incremental streamflow, which is suitable for input to water management models. We also find that the process-conditioning methods improve the timing of the corrected streamflow when conditioned on daily minimum temperature, which we use as a proxy for snowmelt processes