Reference evapotranspiration (ETo) is a complex process in the hydrological cycle that influences the quantity of runoff and then the irrigation water requirements. Many methods have been developed to estimate reference evapotranspiration. The reliable reference evapotranspiration methods are parameter rich models but not applicable for data scarce regions. On the other hand, accuracy and reliability of simple reference evapotranspiration models vary widely according to regional climate conditions. Estimation of reference evapotranspiration of specific area is imperative for investigating, planning, designing, and managing irrigation schemes. The objective of the present study is to determine the ETo values using combination Penman–Monteith model and to devise a model used to predict reference evapotranspiration in the Megecha catchment. In this study, the potential of multiple linear regression model using least squares is investigated in modeling of mean monthly reference evapotranspiration obtained using the standard FAO-56 Penman–Monteith equation. Various combinations of daily climatic data, namely solar radiation, air temperature, relative humidity, and wind speed were used as inputs to the model development. Thus, this can give direction to evaluate the degree and effect of each variable on estimated ETo. In line to this, residual analysis, co-efficient of determination and residual error were used as comparison tools for the evaluation of the model performance. The result of the multiple correlation showed that sun hour, wind speed, and maximum temperature were strongly positive correlation with ETOr = 0.82, 0.71, and 0.78, respectively, whereas relative humidity has moderately negative correlation (r = − 0.604). The multiple linear regression model gave residual errors of 0.26 mm/day and co-efficient of determination of 0.92 for the meteorological station considered. Based on the residual analysis results, it was found that the multiple linear regression model followed a linear trend. The input variables of the model were fitting, and this model could be successfully employed in estimating and predicting the monthly reference evapotranspiration in the study area and can be used in similar parts of data sparse regions.