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As the standard method for estimating reference crop evapotranspiration (ETo), FAO Penman- Monteith (FAO-PM) model incorporates both the thermodynamic aspect and the aerodynamic aspect of evapotranspiration. The model needs complete agricultural meteorological data to estimate ETo, which is considered to be a difficult task in many locations of Hexi Corridor. Meanwhile, the accuracy of the temperature-based models is insufficient. In order to solve these problems, a monthly ETo estimation model (DC-BP-NN) was proposed, which integrated air-temperature, divide and conquer (DC) method and back propagation neural network (BP-NN) with the structure of FAO-PM model. The model consisted of two BP-NN models: the radiation BP-NN model and the aerodynamic BP-NN model. In the experiments, the data was from Jiuquan Weather Station in Hexi Corridor. The reference standard was obtained by FAO-PM model. The results showed that DC-BP-NN model was superior to the other six ETo estimation models, including Blaney-Criddle model, Hargreaves-Samani model, two improved Hargreaves-Samani models, BP-NN model and BP-NN1 model (BP-NN model was based on air temperature and monthly ordinal number), with average root mean square error of 5.99 mm/month, mean bias error of 0.99 mm/month, mean absolute percentage error of 7.18% and determination coefficient of 0.988 6. Therefore, the DC-BP-NN model can be used for estimating monthly ETo in Hexi Corridor with insufficient meteorological data.