Evapotranspiration is the most important part of the hydrological cycle, which plays a key role in water resource management, crop yield simulation, and irrigation scheduling. Therefore, developing a cost-effective and precise model is essential for estimating hourly grass crop reference evapotranspiration (ETo). In this study the potential of the fuzzy inference system (FIS) is investigated as a simple technique for modeling hourly ETo obtained using the FAO-56 Penman-Monteith and ASCE equations. Then, combinations of efficient hourly climatic data namely temperature, wind speed, relative humidity and solar radiation were used as inputs to the fuzzy model. Four fuzzy models were developed based on different combinations of inputs. Common statistics such as Mean square error, average absolute relative error and determination coefficient and two more statistics of Jacovides (t) and R2/t are used as comparison criteria for evaluation of the model performance. Here, Training and testing fuzzy models were done with Fariman meteorological data – an arid region in the northeast of Iran. The fuzzy model whose inputs are solar radiation, air temperature, relative humidity and wind speed, yield the highest correlation and compatibility to reference models of FAO-56 PM and ASCE, based on common statistics. Whereas, the fuzzy model whose inputs are solar radiation, air temperature and relative humidity, are selected as the best model based on combination of common and additional statistics. The fuzzy model with two inputs namely solar radiation and relative humidity has acceptable results, too. The results show that solar radiation is the most effective parameter on hourly reference evapotranspiration and temperature, relative humidity and wind speed were other effective parameters, respectively. These results for training and testing phase are alike. It was found that the developed fuzzy models could be successfully employed in estimating the hourly ETo with a limited weather