This study investigated the capability of M5 Model Tree (M5MT) to predict reference evapotranspiration (ET 0 ). M5MT was trained and tested with climatic data from eight weather stations located in coastal areas of Iran for the years 2000-2008. It was validated with climatic data from seven California Irrigation Management Information System (CIMIS) weather stations for the year 2015. Four different data combinations were utilized to train, test, and validate the M5MT model. These were: daily mean air temperature, wind speed, relative humidity, and solar radiation (configuration 1); daily mean air temperature and solar radiation (configuration 2); daily mean air temperature and relative humidity (configuration 3); and daily maximum, minimum, and mean air temperature, and extraterrestrial radiation (configuration 4). The Penman-Monteith (PM) equation was used as a standard method to provide target ET 0 values. Mean absolute error (MAE), root mean square error (RMSE), and the coefficient of determination (R 2 ) were used to evaluate the performance of the M5MT models developed with different input configurations. Results indicated that M5MT was able to successfully estimate ET 0 . Configuration 1 provided the most accurate results. Configuration 2 showed to have the variables that have a greater influence on ET 0 than configuration 3. Configuration 4 performed the worst. MAE of ET 0 estimates from M5MT 1 was respectively 29%, 55%, and 91% lower than that of M5MT 2 , M5MT 3 , and M5MT 4 , when the model is validated in California. Also, RMSE from M5MT 1 was 29%, 59%, and 125% smaller than that of M5MT 2 , M5MT 3 , and M5MT 4 , respectively.