Long-term dependencies may be one of the reasons for the spatial variability in precipitation frequencies. This study assesses the long-term dependencies in precipitation time series at a basin-scale using the wavelet-based fractal decomposition technique. The gridded precipitation datasets (0.25deg x 0.25deg) from the India Meteorological Department (IMD) for the year, 1901 to 2018 have been used. In order to find the climate change point (i.e., the year in terms of annual series) from each grid point, the mean-based change point detection is performed. Based on the change points, the input for the wavelet analysis is generated into two series, the series -1 (before change point) and the series – 2 (after change point). The results of the climate change points are different for every location, and the corresponding length of the series also gets changed. In order to handle the non-stationarity associated with the time series datasets, the method of wavelet decomposition is used. The Discrete Wavelet Transform (DWT) based fractal decomposition of time series is performed by taking the Daubechies (db1 to db10) mother wavelet along with the varying scale and translation parameters. Both the results of the series wavelet coefficients are compared using the scaled ratio method and the relative shift in the cumulative distribution functions (CDF). Comparing the time series datasets before and after the change point reveals the significance of long-term dependencies at each location. The results of the spatial variability and its patterns explain the long-term dependencies and their significance at a basin-scale, which may support various scientific studies and development.