Display omitted •Adopting a novel methodology called VC-DM for multivariate drought analysis.•Developing LSSVM-SCA as a modern hybridization strategy for downscaling rainfall data.•Using four-dimensional vine copula-based structures (Canonical and Drawable Vines)•Considering climate change conditions in predicting future drought characteristics. This research provides a novel methodology for modeling multivariate dependence structures of meteorological drought characteristics (severity, duration, peak, and interarrival time), based on the combination of four-dimensional Vine Copulas and Data Mining algorithm (hereinafter called VC-DM). Two flexible vine copula structures (i.e., canonical vine (C-vine) and drawable vine (D-vine)) were used for multivariate drought modeling in three climatologically different regions of Iran (i.e., Mehrabad, Semnan, and Nowshahr synoptic stations). Furthermore, data mining algorithms approach was employed for downscaling/bias correction rainfall data obtained from four General Circulation Models (GCMs) (i.e., CanESM2, BNU-ESM, CCSM4, and GFDL-CM3). The approach was based on the least square support vector machine (LSSVM), which is hybridized with two optimization algorithms (i.e., grid search (GS) and Sine Cosine algorithm (SCA)). Results indicated that LSSVM-SCA was more accurate than LSSVM-GS for all GCMs in the testing period. The uncertainty analysis results for the historical period (1977–2005) revealed that LSSVM-SCA had less uncertainty than LSSVM-GS. It was also observed that of all the selected GCMs, CanESM2 had less uncertainty for most climate change scenarios (RCP2.6, RCP4.5, and RCP8.5). So, CanESM2 as the best GCM with low uncertainty and LSSVM-SCA as the superior downscaling/bias correction method were selected for drought characteristics analysis under climate change conditions. The rainfall predictions for the 2021–2100 period were projected to decrease in Mehrabad station and increase in Semnan and Nowshahr stations under all three selected RCPs. Finally, all projected drought characteristics were analyzed by the selected and preferable vine copula-based model (C-vine model with mixed-pair copulas) to provide comprehensive insight towards future drought conditions. It can be concluded that more severe droughts will occur in 2021–2100 than the historical period, and the absolute value of drought severity decreases under RCP2.6 in all stations. All drought durations are also expected to decrease while drought peaks will not change significantly in all stations and under all scenarios.