Climate risk assessments often depend upon downscaled and bias-corrected climate information, making it important to quantify the uncertainties and potential biases of this approach. Sectoral modelers can now choose among several downscaled and bias-corrected ensembles, each constructed by relying on different methodologies and underlying observational datasets, and potentially using a different suite of parent Earth system models. This choice can have important implications for single- and multi-sector outcomes as climate projection uncertainty propagates through the relevant coupled human-natural systems. In this work, we compare projections from five different downscaled and bias-corrected climate model ensembles across two generations of parent CMIP models. We focus on societally-relevant climate and weather extremes in the United States, and elucidate the inter-model as well as inter-ensemble uncertainty. We also discuss whether the large spread in CMIP6 climate sensitivities is important for regional risk assessments after downscaling and bias-correction have been undertaken. Our results provide useful insights for sectoral modelers and can help guide sound choices regarding the use of downscaled and bias-corrected climate information.