## 3 Results
### Responses of GPP to the CHD Events
Figure 3: The spatial distributions of (**a**) 2 m air temperature (T2m, units: K), (**b**) soil moisture (SM, units: m\({}^{-3}\) m\({}^{-3}\)), (**c**) vapor pressure deficit (VPD, units: hPa), and (**d**) downward solar radiation (DSR, units: W m\({}^{-2}\)) for the three CHD years. 0.09 gC m\({}^{-2}\) day\({}^{-1}\) for GPP\({}_{\text{GOSIF}}\) and GPP\({}_{\text{FluxSat}}\), respectively. The EVI\({}_{\text{MODIS}}\) showed consistent results with the two GPP products, with the largest reduction occurring in 2022 (\(-\)0.024), followed by 2003 (\(-\)0.009), and the smallest reduction in 2013 (\(-\)0.002). These results provide important evidence that the responses of GPP to CHD events can vary significantly due to the difference in the intensity of CHD events. The spatial distribution of GPP anomalies further shows the differences in GPP responses to the three CHD years (Figure 4). In 2022, when the reduction in GPP was most severe, both GPP\({}_{\text{GOSIF}}\) and GPP\({}_{\text{FluxSat}}\) showed negative anomalies across most grid points in the YRB, with proportions reaching 76.20% and 85.05%, respectively. The regions with the largest negative GPP anomalies were concentrated in the provinces of Jiangxi, Hunan, Guizhou, and Chongqing. In 2003, fewer grid points showed negative GPP anomalies, with proportions of 47.26% for GPP\({}_{\text{GOSIF}}\) and 66.02% for GPP\({}_{\text{FluxSat}}\), and the magnitude of GPP reduction was also smaller. In 2013, the magnitude of both positive and negative GPP anomalies was relatively small, but there were more positive anomaly grid points than negative ones, ultimately leading to a slight increase in the average GPP in the YRB. The spatial distribution of EVI anomalies is consistent with the two GPP products, particularly GPP\({}_{\text{FluxSat}}\), further confirming the GPP response patterns to the three CHD years. Considering the differences in vegetation characteristics, their responses to extreme events can vary significantly. We further classified the grid points in the YRB into five categories based on their dominant vegetation types: evergreen forests (EF), deciduous forests (DF), mixed forests (MF), shrubs, and crops. Figure 5 and Table 1 show the response of these five vegetation types to the CHD events during the three years. Figure 4: The spatial distribution of vegetation-related anomalies for the three CHD years, 2003, 2013, and 2022. The panels (**a**), (**b**), and (**c**) are for products GPP\({}_{\text{GOSIF}}\), GPP\({}_{\text{FluxSat}}\) (units: gC m\({}^{-2}\) day\({}^{-1}\)), and EVI\({}_{\text{MODIS}}\) (unitless), respectively. In 2022, GPP\({}_{\text{GOSIF}}\), GPP\({}_{\text{FluoSat}}\), and EVI\({}_{\text{MODIS}}\) overall indicate that the GPP reduction was most significant for shrubs, with median values of \(-0.41\) gC m\({}^{-2}\) day\({}^{-1}\), \(-0.64\) gC m\({}^{-2}\) day\({}^{-1}\), and \(-0.03\), respectively. Among the three types of forests, deciduous and mixed forests showed a smaller GPP decrease, even a GPP increase in many grid points, while evergreen forests showed the largest GPP reduction. For crops, the overall GPP reduction was also quite significant, with strong variations between different grid points. For the years 2003 and 2013, the responses of the five vegetation types showed no significant differences, with both negative and positive anomalies, and the medians were near the zero line. Additionally, some discrepancies were observed between the two GPP products, highlighting the inherent uncertainties in the GPP products, which will be discussed further in the following section.