Significance Critical transitions can model abrupt regime shifts in socio-ecological systems. While generic early warning signals that apply across systems have been investigated, no universal signal exists. We therefore propose a data-driven and system-specific approach to developing warning signals. Reddit’s r/place game-experiment provides a rich socio-ecological dataset, which we use to train an original machine-learning-based early warning system that predicts and explains transitions in this human behavioral system. We predict the time-to-transition using system-specific temporal variables, drastically improving upon standard early warning signals. Furthermore, analysis of the predictions provides insight into the dynamics driving these transitions. Our approach can be applied to other complex systems to foresee and understand transitions.