Abstract: Trend-free pre-whitening (TFPW) is a key method for removing the effect of autocorrelation when a non-parametric Mann-Kendall (MK) trend test is used in analysis of river runoff series. However, its rationality and applicability are questioned in the cases that some hydrological changes occur in a changing environment, thus catching researchers’ increasingly attention. In this study, we adopted a hybrid TFPW for data-preprocessing prior to applying the MK trend test to evaluate its performance in analysis of the runoff series featured with hydrological changes, focusing on six detrending measures, i.e. the slope method (SM), EMD method, first order differential method (FD), log-linear detrending method (LLD), 5-point linear moving average method (LMA), and linear regression moving average method (LRMA). This approach is coupled with two pre-whitening methods, one for removing autocorrelation AR(1) only, and the other for removing all the AR(n)s where n stands for all the lag orders of significance. A case study was taken testing the runoff series gauged at the three gauge stations of Dingjiagou, Shenmu and Suide in northern Shaanxi that represent three types of trend changes in the series respectively. These calculations, as results of different hybrid TFPW-MK testing, were compared with those by conventional and modified MK methods. The comparison shows that different hybrid TFPWs give different MK test results of the runoff series. All the detrending methods are good and similar when only AR(1) is removed, while when all the AR(n)s are removed, only LMA and LRMA work satisfactorily and even the SM, if coupled with the conventional TFPW, works poorly deviating far away from the real case. This indicates that among all the TFPW methods, LRMA with AR(n) being retained is the best owing to less autocorrelation information lost in its detrending process and more autocorrelation removed in its pre-whitening. In addition, the conventional TFPW-MK, or SM coupled with the AR(1) removal, can still achieve acceptable results, and the original TFPW procedure is effective only when AR(1) is removed.