In the present study, a hybrid methodology was proposed in which temporal pre-processing and spatial classification approaches were used in a way to take advantage of multiscale properties of precipitation series. Monthly precipitation data (1960–2010) for 31 rain gauges were used in the proposed classification approaches. Maximal overlap discrete wavelet transform (MODWT) was used to capture the time–frequency attributes of the time series and multiscale regionalization was performed by using self-organizing maps (SOM) clustering model. Daubechies 2 function was selected as mother wavelet to decompose the precipitation time series. Also, proper boundary extensions and decomposition level were applied. Different combinations of the wavelet (W) and scaling (V) coefficients were used to determine the input dataset as a basis of spatial clustering. Four input combinations were determined as single-cycle and the remaining four combinations were determined with multi-temporal dataset. These combinations were determined in a way to cover all possible scales captured from MODWT. The proposed model's efficiency in spatial clustering stage was verified using Silhouette Coefficient index. Results demonstrated superior performance of MODWT-SOM in comparison to historical-based SOM approach. It was observed that the clusters captured by MODWT-SOM approach determined homogenous precipitation areas very well (based on physical analysis).