This study aims to comprehensively examine diverse uncertainties/multiplicities (e.g., performance indicators, bias-correction methods, hydrologic models, bias-correction schemes, predictor combinations, watersheds, streamflow magnitudes, and temporal scales) in bias-corrected hydrologic simulations (BCHS). The focus is placed on the variations of BCHS accuracies (representing climatic impacts on runoffs) with every uncertainty, as well as their interactions with the other uncertainties. To achieve this, an integrated bias-corrected hydro-modeling uncertainty analysis approach (IBCHMUA) is developed based on one advanced hydro-modeling method, i.e., discrete principal-monotonicity inference (DiPMI), and two hydrologic models, i.e., Xin’anjiang and HyMOD. IBCHMUA is applied to two representative watersheds (Xiangxi and Zhongzhou) in southern China. Many findings are revealed. For instance, it is necessary to apply multiple performance indicators and DiPMI is effective in correcting hydro-model biases. Every uncertainty poses significant impacts on BCHS, and the significance of the impacts further varies with all or part of the other uncertainties. BCHS accuracies (or the estimated climatic impacts on runoffs in southern China) increase from daily to monthly scales, from Xiangxi to Zhongzhou Watersheds, from the highest through the lowest to the overall runoff magnitudes, from Xin’anjiang to HyMOD models, and from original to bias-corrected hydrologic simulations. Meanwhile, the impacts of the uncertainties in BCHS decrease from bias-correction schemes, temporal scales, streamflow magnitudes, hydrologic models or predictor combinations, to watersheds. These findings are helpful for reducing the complexity and enhancing the reliability of BCHS under diverse uncertainties, and point out the importance of taking into account the interactions of the uncertainties in BCHS studies.