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Properly managing water application in urban plantings is essential throughout arid and semi-arid regions and depends upon consistently accurate calculations of reference evapotranspiration (ETo). The meteorological observations obtainable in city landscapes, however, are rarely sufficient to apply the standard Penman-Monteith methodology for ETo derivation. Intelligent landscape irrigation controllers thus often rely on temperature-driven ETo approximations to automate watering schedules. The present study is directed at crafting deep learning temperature-based ETo predictors and contrasting their accuracy against two widely adopted empirical temperature-based formulas, specifically the FAO Blaney and Criddle (BC) and Hargreaves and Samani (HS). A complementary, freely available, and intuitive software utility, DeepET, was also developed based on the highest-performing deep learning predictors produced in this work. Four classes of artificial neural network (ANN) predictors were assembled, one group operating on raw weather observations and a second group utilizing wavelet-transformed reconstructed signals as input features. Additionally, long short-term memory (LSTM) recurrent neural network (NN) and one-dimensional convolutional neural network (CNN) predictors were built. The dataset underpinning this work was sourced from 101 operational California Irrigation Management Information System (CIMIS) weather stations, yielding more than 725,000 data points spanning 1985-2019. All predictors were benchmarked against the officially reported CIMIS ETo. When tested on previously unseen station records, the temperature-fed DL (Deep Learning) predictors yielded mean absolute errors that were 15%–20% lower than those from the locally calibrated HS equation. Feeding reconstructed wavelet-transform signals into the ANN predictors did not yield any measurable gain. Our findings suggest that DL-based predictors constitute a compelling approach to improving ETo accuracy in urban settings when only temperature readings are available. The DeepET utility is reachable from the Haghverdi Water Management Group portal: http://www. ucrwater.com/software-and-tools.html.