Accurate estimation and reliable universal performance of reference evapotranspiration (ET0) obtained from a few meteorological parameters are important for the rational planning of agricultural water resources and the effective management of water in irrigated regions. Meteorological data in southern China were used to calculate ET0 using the standard Penman–Monteith formula and determined the core decision variable (hours of sunshine, N) and the limited decision variable (relative humidity, RH) using path analysis. Estimation models using an artificial neural network and wavelet neural network were established for the Wuhan and Guangzhou meteorological stations. The statistical indices were positively correlated with the decision contribution rates to ET0. The ET0 values for other stations in southern China were all estimated by these models, which were trained for the Guangzhou station, and then made a total comparison with Hargreaves–Samani (HS) and Priestley–Taylor (PT) empirical ET0 models. Error analysis indicated that the root mean square error and the mean absolute per cent error were around 0.32 mm and 5.5%, respectively, with a high coefficient of determination and Nash–Sutcliffe efficiency over 0.9, indicating that these estimating models could be applied in more regions for universal analysis with high accuracy.