## 2 Methods
### Date Source and Data Preprocessing  
The main data sources in our study are tree-ring indices and GPP products. We collected tree-ring data from seven plots, where six of them were from the International Tree-Ring Databank (ITRDB) and one of them was from our collection in Lincoln Trail State Park, Illinois. All collected tree cores in Lincoln Trail State Park were dried in the laboratory frame hood and were sanded with progressively finer sandpaper (120, 320, and 400 grit). Then, we hand-sanded the samples with 3 M 30 um sanding film until no scratches were visible under the microscope ([PERSON] et al., 2013). The samples were scanned with an Epson V600 scanner at 2400 DPI and measured using the CooRecorder program ([PERSON] et al., 2013) with an output of 0.001 mm precision. We checked the dating of each core against its paired tree-level core (A vs. B) in CooRecorder, built a local master with a kernel of well-dated samples, and checked the date  
Figure 2: The location of our study and tree plot distributions in two phases. Note: (**A,B**) show the location of the study sites within the United States. (**C,D**) indicate the available tree plots in Phase 1 (1895–1937) and Phase 2 (1938–1985). The tree plots in the maps are the following: No. 1: IL018, No. 2: IL030, No. 3: IN012, No. 4: IN013, NO. 5: IN014, No. 6: IN035, and No. 7: our collection. (**C,D**) displays the plots involved in Phase 1 and Phase 2 regression. of the rest of the samples against that kernel. We checked the quality of the measurements and the dating between cores for the whole site in CDendro (17) and COFECHA (18). Then, the raw ring widths were detrended using an age-dependent spline for all chronologies in ARSTAN (18). We cut off the tree-ring index (TRI) when the expressed population signal (EPS) fell below 0.85 (19). Among those seven plots, there were two white oak plots, one sugar maple plot, one shagbark hickory plot, one tulip tree plot, one red oak plot, and one white ash plot (Table 1). Among seven plots, tree-ring indices from four of them were used in Phase 1 reconstruction, and all of them were used in Phase 2. The annual GPP data from 1986 to 2013 were from the Numerical Terradynamic Simulation Group (NTSG) (20), whose spatial resolution was 30 m and temporal resolution was 16 days. The unit of the product was g C/m\({}^{-2}\) year\({}^{-1}\) GPP. For each year, we chose all available GPP images in the growing season (June, July, and August) and generated annual GPP maps by averaging the values for these 3 months. Then, the non-vegetation areas (water, wetland, and built-up areas) were masked out. All data processing mentioned above was via Google Earth Engine. Though the NTSG GPP was the simulated GPP product with the Moderate Resolution Imaging Spectroradiometer (MODIS) and Landsat satellite images, the Landsat series satellite actually provided us with reliable satellite images since 1986. So, in the paper, we call the GPP product \"real data\". After we simulated the GPP data prior to 1986, we called the combination of the simulated data and the real data \"combined data\". In general, there are three main climate drivers to influence vegetation photosynthesis and GPP absorption: pure temperature, pure precipitation, and the combined effect of precipitation and temperature (21). The Palmer Drought Severity Index (PDSI) is a standardized index to evaluate regional dryness with temperature and precipitation. The index ranges from \(-10\) to \(10\), where a positive index indicates more moisture. If the value is lower than \(-2\), we define the weather as drought ([[https://climatedataguide.ucar.edu/climate-data/palmer-drought-severity-index-pdsi](https://climatedataguide.ucar.edu/climate-data/palmer-drought-severity-index-pdsi)]([https://climatedataguide.ucar.edu/climate-data/palmer-drought-severity-index-pdsi](https://climatedataguide.ucar.edu/climate-data/palmer-drought-severity-index-pdsi)) (accessed on 1 September 2024).