In this study, we proposed a Bayesian Vine Copula Machine Learning (BVC‐ML) method to predict streamflow changes in the Yellow River source area based on projections from three GCMs under various climate change scenarios. The BVC‐ML method was to (i) use the vine copula method to reflect the interdependence between the predicted variable (i.e., streamflow) and predictions from different machine learning (ML) techniques, (ii) d...

Drought is one of the most serious natural disasters exacerbated by climate change. Changes in precipitation and temperature in the future increase the likelihood of drought in China. In this study, a stepwise cluster ensemble downscaling (SCED) model was developed to bias‐correct projections of temperature and precipitation from multiple RCM outputs, and further characterized the drought hazards. The developed SCED model was...