Interaction between meteorological and hydrologic processes is challenging to model owing to their high spatio-temporal variability. The understanding of their associations can help to ensure future fresh water security with changing climate. In this study, due to continuously evolving nature of these interactions, the hydrological and meteorological variables are studied on wavelet component level. Multi-Resolution Stationary Wavelet Transformation (MRSWT) is used to transform the independent (climatic variable) and dependent (hydrological variable) time series into their components. The components of the dependent time series are modeled using a kernel-based auto-regressive (AR) model for separating their memory part. The residuals are hypothesized to be the effect of interaction of predictor variables and thus, are modeled using the MRSWT components of meteorological variables in an auto-regressive model with exogenous inputs (ARX). Finally, the predicted residuals (effect of climatic variables) are added to the component estimated by kernel-based AR estimator (memory of dependent series components) to obtain the predicted components of the dependent hydrologic variable, which are then inverse-transformed to obtain the predicted dependent hydrologic variable. The developed hybrid Wavelet-ARX is found to capture the information about relationship between synthetically generated data better than a simple ARX model. The model is then applied to predict total monthly rainfall over Upper Mahanadi Basin and is found to effectively extract the information from the poorly associated hydro meteorological variables. While the potential of Wavelet-ARX is found to be impressive for hydro meteorological applications, additionally, discarding some climatic inputs on the basis of their relative importance may lead to better prediction by the developed model. The developed model is suitable for extracting climatic forcings and is desirable in a changing climate. © 2019 Elsevier B.V.