<p>The prediction of debris flows is relevant because this type of natural hazard can pose a threat to humans and infrastructure. Debris-flow (and landslide) early warning systems often rely on rainfall intensity-duration (ID) thresholds. Unfortunately, no standardized procedures exist for the determination of such ID thresholds, and a validation and uncertainty assessment is often missing in their formulation. As a consequence, updating, interpreting, generalizing and comparing rainfall thresholds is challenging. Using a 17-year record of rainfall and 67 debris flows in a Swiss Alpine catchment (Illgraben), we determined ID thresholds and associated uncertainties as a function of record length. Furthermore, we compared two methods for rainfall definition which consider both triggering and non-triggering events, based on linear regression and/or True Skill Statistic maximization. The main difference between these approaches and the well-known frequentist method is that non-triggering rainfall events were also considered here for obtaining ID-threshold parameters. Depending on the method applied, the ID-threshold parameters and their uncertainties differed significantly. We found that 25 debris flows are sufficient to constrain uncertainties in ID-threshold parameters to ±30 % for our study site. We further demonstrated the change in predictive performance of the two methods if a regional landslide data set was used instead of a local one, with important implications for ID-threshold determination. Furthermore, we tested if the ID-threshold performance can be increased by considering other rainfall properties (e.g. antecedent rainfall, maximum intensity) in a multivariate statistical learning algorithm based on decision trees (random forest). The highest predictive power was reached when the 30-min maximum accumulated rainfall was added to the ID variables, while no improvement was achieved by considering antecedent rainfall for debris-flow predictions in Illgraben. Although the increase in predictive performance with the random forest model was small, such a framework could be valuable for future studies if more predictors are available from measured or modelled data.</p>