Precise and reliable monthly runoff prediction plays a vital role in optimal management of water resources but non-stationarity and skewness of monthly runoff time series can pose major challenges for developing appropriate prediction models. To address these issues, this paper proposes a novel hybrid prediction model based on Elman neural network (Elman), variational mode decomposition (VMD) and Box-Cox transformation (BC), named VMD-BC-Elman model. Firstly, the observed runoff is decomposed into sub-time series using VMD for the better frequency resolution. Secondly, the input datasets were transformed into normal distribution using Box-Cox, and as a result, skewedness in the data was removed and the correlation between the input and output variables enhanced. Finally, Elman is used to simulate the respective sub-time series. The proposed model is evaluated using monthly runoff time series at Zhangjiashan, Zhuangtou and Huaxian hydrological stations in Wei River Basin in China. The model performances are compared with those of single models (SVM, Elman), decomposition-based (VMD-SVM, VMD-Elman et.al) and BC-based models (BC-SVM and BC-Elman) by employing four metrics. The results show that the hybrid models outperform single models, and VMD-BC-Elman model performs best in all considered hybrid models with NSE greater than 0.95, R greater than 0.98, NMSE less than 4.73%, and PBIAS less than 0.39% in both training and testing periods. The study indicates that VMD-BC-Elman model is a satisfactory data-driven approach to predict the non-stationary and skewed monthly runoff time series, representing an effective tool for predicting monthly runoff series.