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Under global warming, a novel category of extreme events has become increasingly apparent, where flood and hot extremes occur in rapid succession, causing significant damages to infrastructure and ecosystems. However, these bivariate compound flood‐hot extreme (CFH) hazards have not been comprehensively examined at the global scale, and their evolution under climate warming remains unstudied. Here, we present the first global picture of projected changes in CFH hazards by using a cascade modeling chain of CMIP6 models, satellite and reanalysis data sets, bias correction, and hydrological models. We find an increasing percentage of floods will be accompanied by hot extremes under climate change; the joint return periods of CFHs are projected to decrease globally, particularly in the tropics. These decreasing joint return periods are largely driven by changes in hot extremes and indicate a likely increase of CFH hazards, and ultimately highlight the urgent need to conduct adaptation planning for future risks.

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Bias correction techniques are widely used to bridge the gap between climate model outputs and input requirements of hydrological models to assess the climate change impacts on hydrology. In addition to univariate bias correction methods, several multivariate bias correction methods were proposed recently, which can not only correct the biases in marginal distributions of individual climate variables but also properly adjust the biased intervariable correlations simulated by climate models. Due to the diversities of climate regime and climate model bias, hydrological simulation for watersheds under different climate conditions may show various sensitivities to the correction of intervariable correlations. Therefore, it is of great importance to investigate (1) whether the correction of intervariable correlations has impacts on the hydrological modeling and (2) how these impacts vary with watersheds under different climate conditions. To achieve these goals, this study evaluates behaviors and their spatial variability of multiple state-of-the-art multivariate bias correction methods in hydrological modeling over 2,840 watersheds distributed in different climate regimes in North America. The results show that, compared to using a quantile mapping univariate bias correction method, applying multivariate methods can improve the simulation of snow proportion, snowmelt, evaporation, and several streamflow variables. In addition, this improvement is more clear for watersheds with arid and warm temperate climates in southern regions, while it is limited for northern snow-characterized watersheds. Overall, this study demonstrates the importance of using multivariate bias correction methods instead of univariate methods in hydrological climate change impact studies, especially for watersheds with arid and warm temperate climates.

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