Abstract: Monthly time series simulation for wind and photovoltaic (PV) power still faces the challenges of the high dimension of random variables and complicated spatial- temporal features. A sequential generative adversarial network (SGAN) based monthly scenario analysis method was proposed for wind and PV power. The representative daily generation states are exploited by a RV-coefficient based clustering technique. The transition regularity of daily generation states was described by Markov Chain. To capture the temporal and spatial features of daily wind and PV power time series, a SGAN was established by introducing scaled dot-product attention mechanism and temporal convolutional network (TCN). A stochastic scenario generation method for monthly wind and PV power time series was subsequently presented. Considering the requirements of medium/long-term power system analyses, a scenario reduction method for monthly wind and PV power time series was developed. Finally, the effectiveness and correctness of the proposed method were verified by the historical power output data of six wind farms and six PV plants located in Northeastern China.