Copyright SIAM. We consider the mining of hidden block structures from time-varying data using evolutionary co-clustering. Existing methods are based on the spectral learning framework, thus lacking a probabilistic interpretation. To overcome this limitation, we develop a probabilistic model for evolutionary co-clustering in this paper. The proposed model assumes that the observed data are generated via a two-step process that depends on the historic co-clusters, thereby capturing the temporal smoothness in a probabilistically principled manner. We develop an EM algorithm to perform maximum likelihood parameter estimation. An appealing feature of the proposed probabilistic model is that it leads to soft co-clustering assignments naturally. To the best of our knowledge, our work represents the first attempt to perform evolutionary soft co-clustering. We evaluate the proposed method on both synthetic and real data sets. Experimental results show that our method consistently outperforms prior approaches based on spectral method.