Since true communities within real-world networks are rarely known, synthetic networks with planted ground truths are an alternative for evaluating community detection methods. Of several available synthetic network generators, Stochastic Block Models (SBMs) produce networks with ground truth clusters that well approximate input parameters from real-world networks and clusterings. However, SBMs can produce disconnected ground truth clusters, even when provided parameters from clusterings where all clusters are connected. Here we describe the REalistic Cluster Connectivity Simulator (RECCS), a technique that creates and then modifies an SBM synthetic network to improve the fit to a given clustered real-world network. Using real-world networks up to 13.9 million nodes in size, we show that RECCS results in synthetic networks that have a better fit to cluster edge connectivity than their starting SBMs, while providing roughly the same quality fit for other network and clustering parameters as unmodified SBMs.