## 3 Results
### How do Meteorological Factors Contribute to the Relationships? Meteorological factors like radiation, SM, and VPD impacted the photosynthetic center and affected the distribution of absorbed light energy by leaves. While PAR was the most critical factor affecting SIF and GPP at different time scales (highest Pearson's correlation coefficient of 0.872 for SIF at the 30 min scale), VPD and SM also had significant positive correlations with SIF on the 30 min timescale (Table 1). On the daily scale, PAR was positively related to SIF and negatively to GPP, most of the negative correlation was due to the growth rhythm, the significantly lower GPP was observed in early June at the same PAR level (Figure 1 and Figure S1). It was also the only meteorological factor that affected both SIF and GPP on the daily scale. Consequently, we drew a conclusion that the seasonal variations of SIF and GPP were primarily driven by the change in PAR that accounted for most of the variations in SIF and GPP. VPD only had a positive correlation with SIF on the 30 min timescale and no correlation on the daily scale. On the daily scale, VPD and GPP had a significant negative correlation (Table 1). SM had an obvious diurnal variation trend on the seasonal scale, but had a smaller diurnal variation during non-rainy days (Figure 1). SM had a significant correlation with SIF on both the 30 min and the daily scale, and the correlation increased with the aggregation. However, it had no correlation with GPP on either time scale. On the 30 min scale, the difference in meteorological factors affecting SIF and GPP was mainly reflected in the correlation between changes in VPD and SM with SIF, but there was no such relationship with GPP. On the daily scale, the meteorological factors affecting SIF and GPP were quite different. The relationship between PAR with SIF and GPP shows the opposite pattern. Besides PAR, VPD was the main factor that affected GPP while SM had a great impact and a significant positive correlation with SIF on the daily scale. Figure 4: Relationships between the daily slopes of the linear regressions between SIF and GPP symbolized by GPP/SIF in 30 min timescale and daily mean PAR. The black line is linear regression, K and R\({}^{2}\) represent the slope and coefficient of determination of linear regression, respectively. The color bar on the right on behalf of the R\({}^{2}\) of the linear regressions in every single day. All data are for sunny days only. Therefore, we analyzed the influence of meteorological factors on the slope of linear regression between SIF and GPP on a single day to understand why the relationship deteriorated with aggregation. The impact of PAR on SIF and GPP was not only reflected in its significant correlation with both of them, but also in its impact on the slope of SIF and GPP in data from all days. In all sunny day data, PAR had a significant linear correlation with the daily slope (Figure 4). With the increase of PAR on the daily scale, the slope between the SIF and GPP gradually decreased, and R\({}^{2}\) was 0.629, indicating that PAR can explain 62.9% of the variations in the slope between SIF and GPP. The variation of PAR on a seasonal scale may be considered as the reason for the dispersion of SIF and GPP points on sunny days. Therefore, the correlation weakened with aggregation. We analyzed the relationship between different meteorological factors and SIF and GPP on a 30 min timescale. With an increase in PAR, both SIF and GPP showed different trends. When PAR was lower than 1250 \(\upmu\)mol\(\cdot\)m\({}^{-2}\cdot\)s\({}^{-1}\), both SIF and GPP increased with an increase in PAR, and GPP reached a maximum of 0.316 g C\(\cdot\)m\({}^{-2}\cdot\)30 min\({}^{-1}\) when PAR was 1250 \(\upmu\)mol\(\cdot\)m\({}^{-2}\cdot\)s\({}^{-1}\). When PAR exceeded 1250 \(\upmu\)mol\(\cdot\)m\({}^{-2}\cdot\)s\({}^{-1}\), the relationship between GPP and PAR showed an opposite trend, and decreased significantly with an increase in PAR.