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A novel data-driven method of modal analysis for complex flow dynamics, termed as reduced-order variational mode decomposition (RVMD), has been proposed, combining the idea of the separation of variables and a state-of-the-art nonstationary signal-processing technique -- variational mode decomposition. It enables a low-redundant adaptive extraction of coherent structures in statistically nonstationary flows, with its modes computed by solving an elaborate optimization problem using the block coordinate descent algorithm. Discussion on the intrinsic relations between RVMD and some classic modal decomposition methods demonstrates that RVMD can be reduced into proper orthogonal decomposition (POD) or discrete Fourier transform (DFT) at particular parameter settings. The significant advantages of RVMD for performing time-frequency analysis are highlighted by a signal-processing analogous categorization of the widely-used modal decomposition techniques. It is also confirmed that the combination of RVMD and the Hilbert spectral analysis provides a physically intuitive way to explore the space-time-frequency characteristics of transient dynamics. Finally, all the appealing features of RVMD mentioned above are well verified via two canonical flow problems: the transient cylinder wake and the rectangular turbulent supersonic screeching jet.