Decision-makers urgently need fit-for-purpose, actionable climate information to address worsening risks to water security due to climate change. To help address this need, we pioneer new work that builds on a recently established standardized benchmarking framework to assess downscaled precipitation simulations and explore how benchmarking historical, fundamental model performance impacts confidence in regional precipitation projections. Inspired by frameworks used by the Coupled Model Intercomparison Project (CMIP) community to navigate the “ensemble of opportunity” that explore the spread, independence, and performance of CMIP models, we assess future confidence across three related categories: model agreement, dependence, and performance. Using data from the Coordinated Regional Downscaling Climate Experiments (CORDEX)-Australasia domain and Southern Australia as a case study, we highlight how benchmarking only “fundamental,” historical model performance improves confidence in regional precipitation projections across these categories of confidence. We find that including fundamentally flawed models in an ensemble can indicate a false level of confidence and model agreement in regional climate projections in annual and wet-season total rainfall and rainfall extremes. We find that our benchmarked subset reduces the spread in projections across season-rainfall index combinations without underestimating observed natural variability or end-of-century uncertainties due to the climate change response. By taking a novel approach to address the coupled interdependencies between global climate models and regional climate models in our dynamically downscaled ensemble, we find that we can increase confidence though historical benchmarking spatially and temporally without over-constraining plausible future projections.