<p>Drought exhibits complex and multivariate characteristics, making it crucial to identify drought event features reliably and comprehensively, as well as accurately characterize the dependency structure between these features for drought risk management. In this study, we selected the optimal datasets for the upper and middle reaches of the Huai River Basin using remote sensing satellites, reanalysis, interpolated precipitation datasets, and potential evapotranspiration datasets. We utilized the Standardized Precipitation Evapotranspiration Index (SPEI) to calculate drought indices. A three-dimensional identification method was employed to determine drought duration, drought area, and drought severity. To assess meteorological drought risk in the basin from 1981 to 2022, we applied the Regular Vine copula. The results of the study revealed that: (1) The optimal grid datasets for the basin are the Precipitation reconstruction over land (PRECL) and Hourly potential evapotranspiration (HPET). (2) The identification results obtained through the three-dimensional meteorological drought method in this study are broadly reliable. (3) Compared to traditional multivariate copulas, the Regular Vine copula offers a more precise characterization of the dependency structure between drought characteristic variables. (4) In order to completely characterize the probability of drought risk in a watershed, multiple variables (especially drought area) that characterize drought events should be fully considered. (5) The basin is prone to large-area, short-duration, low-severity meteorological drought events. This study evaluates the probability of drought characteristics in the basin, offering valuable insights for regional water resource allocation and drought risk management.</p>