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Abstract Irrigation can increase crop yields and could be a key climate adaptation strategy. However, future water availability is uncertain. Here we explore the economic costs and benefits of existing and expanded irrigation of maize and soybean throughout the United States. We examine both middle and end of the 21st-century conditions under future climates that span the range of projections. By mid-century we find an expansion in the area where the benefits of irrigation outweigh groundwater pumping and equipment ownership costs. Increased crop water demands limit the region where maize could be sustainably irrigated, but sustainably irrigated soybean is likely feasible throughout regions of the midwestern and southeastern United States. Shifting incentives for installing and maintaining irrigation equipment could place additional challenges on resource availability. It will be important for decision makers to understand and account for local water demand and availability when developing policies guiding irrigation installation and use. 

The US Drought Monitor (USDM) is a hallmark in real time drought monitoring and assessment as it was developed by multiple agencies to provide an accurate and timely assessment of drought conditions in the US on a weekly basis. The map is built based on multiple physical indicators as well as reported observations from local contributors before human analysts combine the information and produce the drought map using their best judgement. Since human subjectivity is included in the production of the USDM maps, it is not an entirely clear quantitative procedure for other entities to reproduce the maps. In this study, we developed a framework to automatically generate the maps through a machine learning approach by predicting the drought categories across the domain of study. A persistence model served as the baseline model for comparison in the framework. Three machine learning algorithms, logistic regression, random forests, and support vector machines, with four different groups of input data, which formed an overall of 12 different configurations, were used for the prediction of drought categories. Finally, all the configurations were evaluated against the baseline model to select the best performing option. The results showed that our proposed framework could reproduce the drought maps to a near-perfect level with the support vector machines algorithm and the group 4 data. The rest of the findings of this study can be highlighted as: 1) employing the past week drought data as a predictor in the models played an important role in achieving high prediction scores, 2) the nonlinear models, random forest, and support vector machines had a better overall performance compared to the logistic regression models, and 3) with borrowing the neighboring grid cells information, we could compensate the lack of training data in the grid cells with insufficient historical USDM data particularly for extreme and exceptional drought conditions.