## 4 Conclusions  
The influence of summer meteorological conditions on GPP was identified using satellite-based temperature, precipitation indices, and GPP data at various scales (i.e., pixel, county, and state levels). The distinct characteristics of the regional climate impact were precisely assessed as the scale increased from state level to 0.05\({}^{\circ}\) resolution. In investigating the relationship across the variables over the twelve major crop-producing states in CONUS, the temperature and precipitation indices exhibit a strong linear relationship (r > 0.7) around the central United States. Consistently, the variation of GPP also exhibits a higher relationship with the summer TCI and PCI in the central United States, while GPP shows a relatively higher linear relationship with TCI (r\(\sim\)0.9) than PCI (r\(\sim\)0.75). Remarkably, their relationship was more robust in the Great Plains, which represent a semi-arid background climate. In seeking the factor affecting the regionality of GPP sensitivity to meteorological conditions, it was the long-term mean climate that determines GPP sensitivity to meteorological conditions primarily due to the non-linear relationship between them. The regional impact of the variation of meteorological conditions on the maize and soybean yields was investigated, as was whether the regional dependency in the relationship between meteorological conditions and GPP also exists in maize and soybean yields. The correlations between GPP and maize and soybean yields are high in the majority of states, showing that the impact of meteorological conditions on GPP is similar to that on maize and soybean yields. Maize yield appeared to have a stronger association with GPP than soybean production because maize has a significantly higher RUE value than soybeans. Maize and soybean yields tend to be more sensitive to temperature variations than to precipitation variations. Consistent with the relationship between GPP and meteorological conditions, maize and soybean yields had a stronger correlation with TCI and PCI in the Great Plains. This indicates that a more vulnerable crop growth in a hot extreme is expected in the Great Plains with a more sensitive GPP response [16]. This study still has some limitations. This study used statistical methods at the statistical-descriptive level, and the analysis focused on calendar timing rather than crop timing. Future studies will implement crop models to describe the mechanisms in-depth and consider the phenology, the lengths of the soybean and maize crop cycles, and other factors. The regional dependence would provide useful insights into the more intense, frequent, and long-lasting heat extremes expected in the future climate [40]. In conclusion, this study suggests that more research into the climate-crop yield relationship at the local scale is required. Greater knowledge of the relative roles of regional temperature and precipitation on crop development would help a variety of agricultural applications. Figure 7: Scatter plot of TCI–crop yield relationship (\(x\)-axis) and PCI–crop yield relationship (\(y\)-axis) at the state-level data from (**a**) maize and (**b**) soybean, respectively, averaged for July and August. In Figure 7b, the correlation for Wisconsin is omitted due to the lack of data. **Author Contributions:** Conceptualization, C.Y., D.K. and S.P. ; formal analysis, C.Y., D.K. and S.P. ; investigation, C.Y., D.K. and S.P. ; methodology, C.Y., D.K. and S.P. ; supervision, S.P. ; writing--original draft, C.Y., D.K. and S.P. ; writing--review and editing, S.P. ; funding acquisition, S.P. All authors have read and agreed to the published version of the manuscript. **Funding:** This work was supported by Seoul National University of Science and Technology. **Data Availability Statement:** Not applicable. **Conflicts of Interest:** The authors declare no conflict of interest. * _[PERSON] et al.