Abstract In NLG systems, temporal uncertainty in raw data can hamper the inference of temporal and causal relationships between events and thus impact the quality of the generated texts. In this paper, we introduce a framework to represent and reason with temporal uncertainty based on possibility theory and propose a model that uses the outcomes of such temporal reasoning to select linguistic expressions to convey uncertainty to the reader. Our model is based on Fuzzy Temporal Constraint Networks (FTCN) and our work is based on the assumption that uncertainty should be communicated to an end user. The model we propose is grounded in experimental data from three languages. We present a large-scale empirical study that investigates the conditions that influence human subjective uncertainty in reasoning about temporal relations. Based on this, we also construct a classifier to select expressions to convey uncertainty, based on possibility and necessity values. We then present an evaluation which shows that the predictions of the FTCN model correlate well with human subjective uncertainty in different scenarios. An evaluation of our temporal expressions classifier also suggests good results, compared to human selection of linguistic expressions, as compared to baseline models.