In this work, we propose a data-driven framework to identify precursors of extreme events in turbulent reacting flows. Specifically, we tackle the problem of flashback prediction in a lean hydrogen reheat combustor. Our framework is composed of two parts. The first consists in the use of a co-kurtosis based approach to identify the components of the thermochemical and flow state which are the most relevant for the onset of flashback. This allows for an efficient low-dimensional representation. From this reduced representation, a modularity-based clustering algorithm is then employed to segregate between clusters which contain normal and extreme (flashbacking) states, and the cluster located in-between these states, which are the precursor states of extreme events. We show that this method is able to identify the most important features at the onset of flashback in the considered reheat combustor and then provide precursor states based on those. The prediction time obtained with the identified precursors is relatively large when compared to the duration over which the combustor is stable. Additional analyses on the specific choice of features for the precursor identification and the sampling locations are made. The robustness of the method when using shorter time series to identify the precursor is also investigated. Results show that the method is generally robust with respect to such changes. A first step towards practical measurements is also attempted with wall pressure measurements, which shows only a moderate reduction in prediction time. This work proposes for the first time a data-driven technique to automatically identify precursors of flashback in hydrogen combustion opening the path for such applications on other extreme events in reacting flows.