## 2 Data and Methodology  
The considered study area CONUS comprises 48 states and the District of Columbia. This study was conducted using LST, precipitation, GPP, and crop yield data across CONUS (Table 1). Moderate resolution imaging spectroradiometer (MODIS) provides various surface products which are useful to monitor crop growth. Monthly MODIS daytime Terra satellite LST data (MOD11C3) in summer were used in this study. The LST data are obtained at about 10:30 a.m. local time and have 0.05\({}^{\circ}\) spatial resolution. The generalized split-windowalgorithm was used for retrieving the LST products [26]. GPP data based on daily net photosynthesis was used to diagnose the condition of the growth of crops (maize and soybean). In this study, an 8-day composite MODIS GPP product (MOD17A2) at daytime (10:30 a.m.) with 1 km resolution was used. The MODIS LST and GPP products for July and August from 2000 to 2020 were downloaded from NASA Earthdata Search ([[https://lpdaac.usgs.gov/tools/earthdata-search](https://lpdaac.usgs.gov/tools/earthdata-search)]([https://lpdaac.usgs.gov/tools/earthdata-search](https://lpdaac.usgs.gov/tools/earthdata-search)); assessed on 1 February 2022). Monthly precipitation data for July and August from 2000 to 2020 was obtained from Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) ([[https://www.chc.ucsb.edu/data](https://www.chc.ucsb.edu/data)]([https://www.chc.ucsb.edu/data](https://www.chc.ucsb.edu/data)); assessed on 1 February 2022), which combines satellite imagery with 0.05\({}^{\circ}\) resolution and in situ station data to produce a gridded rainfall time series [27]. CHIRPS especially shows good agreements with the station data in CONUS [28]. The MODIS GPP data with 1 km resolution were mean aggregated to 0.05\({}^{\circ}\) resolution and transformed to a monthly time scale by considering the number of days in the composite data. Precipitation data were reprojected onto MODIS imagery. Crop yield data from 2000 to 2020 were obtained from the US Department of Agriculture National Agricultural Statistics Service Quick Stats (USDA NASS; [[https://quickstats.nass.usda.gov/](https://quickstats.nass.usda.gov/)]([https://quickstats.nass.usda.gov/](https://quickstats.nass.usda.gov/)); assessed on 1 February 2022). Total maize (grain) and soybean yield data from survey data measured in units of bushels per acre were used. The bsh/ac can be converted to tonnes/hectare (t/ha) using a conversion factor of 0.063 and 0.067 for maize and soybean, respectively. Figure 1 shows the long-term mean spatial distribution of LST and precipitation averaged from July to August (Figure 1a,b), the amount of state maize and soybean production (Figure 1c,d), and the irrigation rate (Figure 1e). The percentages of US maize and soybean production are indicated for each state (Figure 1f). The Midwest shows relatively lower LSTs in summer compared with other regions, while the western US exhibits high LSTs due to low evaporation from their dry condition (i.e., arid, desert, or steppe climate) [29]. For precipitation, the eastern US has a huge amount of rainfall in the summer, particularly in the southeastern US. The large rainfall in these regions is associated with the moisture transport from the Gulf of Mexico via the Caribbean low-level jet [30]. The Midwest also exhibits relatively large summer rainfall, due to moisture convergence based on the Great Plains low-level jet [31]. With the cool and rainy background climate, the major regions for maize and soybean production are located in the midwestern maize/soybean belt area (Figure 1). The states that make up 80% of total maize and soybean production are highlighted with a specific boundary (Figs. 1c and e). Nine states--South Dakota (SD), Nebraska (NE), Kansas (KS), Minnesota (MN), Iowa (IA), Wisconsin (WI), Illinois (IL), Indiana (IN), and Ohio (OH)--for maize, and eleven states--SD, NE, KS, MN, IA, IL, IN, OH, North Dakota (ND), Missouri (MO), and Arkansas (AR)--for soybean are included in these boundaries.