<p>Large-scale scientific simulations increasingly adopt statistical distribution representation for lightweight data storage and analysis. We propose a method for selecting key time-steps of time-varying scientific data based on parametric-distribution models. Our solution establishes a similarity measurement method for complex distribution models of scientific datasets. The distribution similarity serves as the criterion for the hierarchical clustering to generate a set of short and continuous time intervals. We then iteratively merge these time intervals according to the deviation vector representation of the distribution model. The deviation vector is used as termination iteration conditions to obtain the final time intervals from which representative time steps can be selected. Our solution can achieve automated selection without explicitly feature defining or labeling, while significantly reducing computational overhead with comparable quality. We demonstrate the effectiveness of our approach based on the distribution space through five different real-world scientific applications.</p>