Compound dry-hot extremes (CDH) cause devastating socio-economic impacts on society and ecosystems. Univariate risk assessment might not catch the potential impacts of CDH. Considering the negative correlation of average summer temperature and precipitation, this study developed two kinds of copula-based likelihood multiplication index, such as the whole likelihood multiplication index ( \({LMI}_{whole}\) ) and the second likelihood multiplication index ( \({LMI}_{2}\) ), to enumerate the attribution and projection of compound dry-hot events in Yellow River Basin (YRB) of China. Observed datasets show that the areas influenced by the CDH within YRB are increasing, and the most-extreme CDH event was observed several decades ago. Comparison of natural-only and all forcing-based CMIP6 data show that anthropogenic factors are causing the increasing trend of affected areas. Results of analysis showed that changes in the risk of CDH events could depend on not only the temporal variation of the marginal distribution but also the change in the dependence structure between variables. By comparing the difference between \({LMI}_{whole}\) and \({LMI}_{2}\) , all grid-based areas in YRB were classified into five cases of combined effects which would help identify the synergistic and non-synergistic contribution of variations in marginal distribution and dependence structure of variables on risk of CDH events. Under Shared Socioeconomic Pathways scenarios (SSPs), the likelihood of concurrent dry-hot extremes is expected to increase in more than half of YRB while small parts of YRB would also experience decreasing likelihood of CDH events with 38.2% of grids under SSP1-2.6, 37.4% under SSP3-7.0 and 36.9% under SSP5-8.5 belonging to case 3 and 6.
