The radar cross section (RCS) is an important physical parameter to characterize the scattering characteristics of targets. This work aims at the problem that the typical fluctuation models have low precision in describing the RCS distribution characteristics of stratospheric aerostats, and the Gaussian mixture model is applied to fit the dynamic RCS distribution for aerostats. Firstly, the line-of-sight (LOS) angle for radar in aerostat’s body coordinates is formulated. Secondly, a superposition of several Gaussian models is used to characterize the probability density distribution of aerostat’s RCS, and the model parameters are approximated via the expectation maximization algorithm. Finally, the fitting effect of Gaussian mixture model on RCS distribution is studied and tested with some aerostat's RCS measurements and compared to typical fluctuation models. The results show that the Gaussian mixture model can improve the fitting effect of RCS probability distribution by up to 96.87% compared with other typical models under the least square criterion, thus demonstrating the effectiveness of Gaussian mixture model.