[Objective] The large-scale integration of distributed renewable energy generation （REG） has significantly enhanced the flexible regulation capabilities of distribution systems. However， the inherent randomness and volatility of REG output characteristics present serious challenges to the security and stability of distribution system operations. [Methods] To effectively improve the adaptability of day-ahead dispatch plans to uncertainties， this study proposes a distributionally robust day-ahead dispatch optimization method for active distribution networks （ADN） based on an improved conditional generative adversarial network （CGAN）. First， an improved CGAN model designed by three-dimensional convolution （Conv3D） is proposed to address the problem of generating day-ahead scenarios for wind turbines （WT） and photovoltaic （PV） outputs considering spatio-temporal correlation， which effectively reduces the conservatism of the generated scenario set. Second， based on the generated day-ahead scenario samples of the WT and PV outputs， a Wasserstein ambiguity set construction method based on kernel density estimation （KDE） is proposed， which realizes full utilization of the sample distribution information. On this basis， a two-stage distributionally robust day-ahead dispatch optimization （DRO） model for ADN is established， considering multiple grid-side resource coordination. The original model is reconstructed into a mixed-integer linear programming problem to obtain a solution based on the affine strategy and strong duality theory. [Results] The findings demonstrate that although the day-ahead dispatch plan cost of the proposed method increases by 1.87% and 0.21% compared with the deterministic optimization （DO） and stochastic optimization （SO） methods， the integrated operation cost decreases by 5.38% and 0.46% under the worst-case scenario， respectively. [Conclusions] The analysis revealed that the proposed DRO model exhibits better adaptability to REG uncertainty and can effectively decrease the operational adjustment cost of the day-ahead dispatch plan while maintaining robustness， especially under the worst-case scenario.