The reference evapotranspiration (ETo) pertains to the evapotranspiration of cold-season grasses with an approximate height of 0.12 m or full-covered alfalfa with a height of 0.50 m. Accurate short-term ETo forecasts are indispensable for informed irrigation decisions by relevant departments and individuals. Four deep learning (DL) models, including Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Bidirectional LSTM (Bi-LSTM), and Bidirectional GRU (Bi-GRU), as well as two calibrated empirical models (Hargreaves-Samani (HS) and reduced-set Penman–Monteith (RPM)), were used to evaluate the performance of the ETo forecast with a lead time of 1–7 d using temperature forecasts in different climates. The results reveal that the DL models and calibrated HS and RPM models exhibited comparable trends in the ETo forecasts for lead times of 1–7 d. Nonetheless, the DL models consistently outperformed the HS and RPM models across the diverse climatic regions in China. The DL models displayed an average root mean square error (RMSE) and mean absolute error (MAE) of less than 0.887 and 0.633 mm/d, respectively. Moreover, the mean correlation coefficient (R) and accuracy (ACC) exceeded 0.807% and 89.701%, respectively. Among the DL models, the LSTM model demonstrated slightly superior performance in short-term daily ETo forecasts in diverse climates. The LSTM model exhibited RMSE and MAE ranges of 0.563–0.875 mm/d and 0.418–0.626 mm/d, respectively, along with R and ACC ranges of 0.81–0.90 and 89.94–98.11%, respectively. Furthermore, even with an increase in lead time, the DL models continued to exhibit strong predictive capabilities, consistently surpassing the performance of the HS and RPM models. Overall, the trained DL models presented an exceptional ability to forecast the short-term daily ETo in various climatic regions of China. These models require only a few input variables and readily available data, making them highly advantageous for practical applications in ETo forecasting. Such models hold promise for significantly enhancing regional agricultural water-resource planning and management.
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