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Abstract Many segmentation or change‐point detection methods for homogenizing climate time series compare candidate station data with reference data to eliminate common climate signals and more efficiently detect spurious, non‐climatic changes. One drawback is that it is difficult to decide whether the detected change‐point is due to the candidate series or to the reference. A so‐called attribution procedure is typically applied in a post‐processing step for each detected change‐point. This article describes a new statistical method for the attribution of change‐points detected in Global Navigation Satellite System (GNSS) minus reanalysis series of integrated water vapour. It requires at least one nearby station with similar GNSS and reanalysis data. Six series of differences are formed from the four base series (BS) and are tested for a significant jump at the time of the change‐point detected in the candidate station. The six test results are analysed with a statistical predictive rule to attribute the change‐point to one, or several, of the four BS. Original aspects of our method are: (1) the significance test, which is based on a generalized linear regression approach, taking both heteroscedasticity and autocorrelation into account; (2) the predictive rule, which uses a machine learning method and is constructed from the test results obtained with the real data by using a resampling strategy. Four popular machine learning methods have been compared using cross‐validation and the best one was applied to a real data set (49 main stations with 114 change‐points). The results depend on the choice of the test significance level and the aggregation method combining the prediction results when several nearby stations are available. We find that 62% of the change‐points are attributed to GNSS, 19% to the reanalysis, and 10% are due to coincident detections.
Nguyen et al. (Tue,) studied this question.