An abrupt change occasionally occurs when the dynamical system suddenly shifts from one stable state to a new state, which can take place in many complex systems, such as climate, ecosystem, social system, and so on. In order to detect abrupt change, this article presents a novel method – sliding transformation parameter (STP) on the basis of skewness change and the Box–Cox transformation. Tests on model time series and 1000 simulated daily precipitation data show the ability of the present method to identify and detect abrupt change of probability density function. The applications of STP in daily precipitation data show that there is an abrupt climate change between 1979 and 1980 in the selected observational stations, which is almost the same with the result obtained by approximate entropy (ApEn). Furthermore, it is found that the sample sizes of sliding windows have some influence on the Lambda parameter of the Box–Cox transformation, but it does not significantly affect the varying trend of the parameter and the identification of the change point in annual or interannual time scale. Comparing STP with the coefficient of skewness and kurtosis, ApEn, and some statistics approaches (e.g. percentiles and annual maxima), we find that the performance of the present method is much better than that of these methods.