Abstract. Integrated water vapour (IWV) measurements from similar or different techniques are often inter-compared for calibration and validation purposes. Results are usually assessed in terms of bias (difference of the means), standard deviation of the differences, and linear fit slope and offset (intercept) estimates. When the instruments are located at different elevations, a correction must be applied to account for the vertical displacement between the sites. Empirical formulations are traditionally used for this correction. In this paper we show that the widely used correction model based on a standard, exponential, profile for water vapour cannot properly correct the bias, slope, and offset parameters simultaneously. Correcting the bias with this model degrades the slope and offset estimates and vice versa. This paper proposes an improved correction method that overcomes these limitations. It implements a multiple linear regression method where the slope and offset parameters are provided from a radiosonde climatology. It is able to predict monthly mean IWVs with a bias smaller than 0.1 kg m−2 and a root-mean-square error smaller than 0.5 kg m−2 for height differences up to 500 m. The method is applied to the inter-comparison of GPS IWV data in a tropical mountainous area and to the inter-validation of GPS and satellite microwave radiometer data. This paper also emphasizes the need for using a slope and offset regression method that accounts for errors in both variables and for correctly specifying these errors.