This study compares two different adaptive neuro-fuzzy inference systems, adaptive neuro-fuzzy inference system (ANFIS) with grid partition (GP) method and ANFIS with subtractive clustering (SC) method, in modeling daily reference evapotranspiration (ET 0 ). Daily climatic data including air temperature, solar radiation, relative humidity and wind speed from Adana Station, Turkey were used as inputs to the fuzzy models to estimate daily ET 0 values obtained using FAO 56 Penman Monteith (PM) method. In the first part of the study, the effect of each climatic variable on FAO 56 PM ET 0 was investigated by using fuzzy models. Wind speed was found to be the most effective variable in modeling ET 0 . In the second part of the study, the effect of missing data on training, validation and test accuracy of the neuro-fuzzy models was examined. It was found that the ANFIS-GP model was not affected by missing data while the test accuracy of the ANFIS-SC model slightly decreases by increasing missing data’s percent. In the third part of the study, the effect of training data length on training, validation and test accuracy of the ANFIS models was investigated. It was found that training data length did not significantly affect the accuracy of ANFIS models in modeling daily ET 0 . ANFIS-SC model was found to be more sensitive to the training data length than the ANFIS-GP model. In the fourth part of the study, both ANFIS models were compared with the following empirical models and their calibrated versions; Valiantzas’ equations, Turc, Hargreaves and Ritchie. Comparison results indicated that the three-and four-input ANFIS models performed better than the corresponding empirical equations in modeling ET 0 while the calibrated two-parameter Ritchie and Valiantzas’ equations were found to be better than the two-input ANFIS models.