Potential evapotranspiration (ETp), defined as the amount of water that would be lost by evaporation and transpiration from an area in the absence of water limitation, is an import hydrometeorological variable. Accurate estimation ETp is critical for a wide range of applications including predictions of, irrigation water requirement, groundwater recharge, stream discharge, drought and wildfires. Long-term change in ETp is considered an indicator of the impact of climate change on ecosystem functioning. A wide range of physically-based and empirical models have been developed to estimate ETp. These methods can be explained in terms of their complexity data requirements. The most complex and demanding models (e.g., Penmann-Montheith) require measurements of radiation, temperature, windspeed, and vapor pressure and have been shown to provide very close approximation of physically measured ET from unstressed systems. On the other extreme, the simplest models require only temperature data (e.g., Thornthwaite, 1948) and are the most commonly implemented. However, without site-specific calibration, methods that depend solely on temperature achieve only modest accuracy. Here we present a machine-learning (ML) approaches that utilize hourly and sub-hourly temperature records to produce predictions that are comparable with the more complex methods that require full meteorological datasets. Specifically, we show that ML algorithms can learn the patterns of temperature fluctuations that are related to attenuation of potential solar radiation. We anticipate the approach developed here will be valuable for estimation of historical ETp as well as for short-term forecasting using temperature forecasts.