Hydrological data in general and water quality (WQ) data in particular frequently suffer from missing records and/or short-gauged monitoring/sampling sites. Many statistical regression techniques are employed to substitute missing values or to extend records at short-gauged sites, such as the Kendall-Theil robust line (KTRL), its modified version (KTRL2), ordinary least squares regression (OLS), four MOVE techniques, and the robust line of organic correlation (RLOC). In this study, in aspiring to achieve better accuracy and precision, the À Trous-Haar wavelet transform (WT) was adopted as a data denoising preprocessing step prior to applying record extension techniques. An empirical study was performed using real WQ data, from the National WQ monitoring network in the Nile Delta in Egypt, to evaluate the performance of these eight record-extension techniques with and without the WT data preprocessing step. Evaluations included the accuracy and precision of the techniques when used for the restoration of WQ missing values and for the extension of the WQ short-gauged variable. The results indicated that for the restoration of missing values, the KTRL and WT-KTRL outperformed other techniques. However, for the extension of short-gauged variables, WT-KTRL2, WT-MOVE3, and WT-MOVE4 techniques showed more accurate and precise results compared with both other techniques and their counterparts without the WT.