## 3 Results
### The Independent Effects of RSM and PRE on GPP
Notably, the magnitude of growth differed from that derived from GOSIF GPP data, potentially attributable to the MODIS dataset's greater incidence of missing values and reduced sample size, which may have introduced variability (Figure S3a,b). Overall, the analysis corroborated that RSM exerts a stronger facilitative influence on GPP than PRE throughout the study region (Figure S3c,d). We investigated the pixel-level effects of RSM and PRE on GPP. Positive values of \(\Delta SA_{GPP}(SA_{RSM}\mid SA_{PRE})\) and \(\Delta SA_{GPP}(SA_{PRE}\mid SA_{RSM})\) were observed in 78.12% and 65.53% of the total pixels, respectively, with \(SA_{RSM}\) exerting a more pronounced influence than \(SA_{PRE}\) across 62.13% of the study area. This indicates that increased \(SA_{RSM}\) more effectively promotes GPP enhancement compared to elevated \(SA_{PRE}\) (Table 1). Furthermore, the mean values of \(\Delta SA_{GPP}(SA_{RSM}\mid SA_{PRE})\) values consistently surpassed those of \(\Delta SA_{GPP}(SA_{PRE}\mid SA_{RSM})\) across all land use types (Figure 7b,d). Both \(\Delta SA_{GPP}(SA_{RSM}\mid SAP_{PRE})\) and \(\Delta SA_{GPP}(SA_{PRE}\mid SA_{RSM})\) values were relatively low in forests and wetlands, likely because forest ecosystems exhibited greater ecological complexity where GPP resulted from multiple interacting factors, making it less sensitive to variations in either RSM or PRE. The parameter \(\Delta SA_{GPP}(SA_{PRE}\mid SA_{RSM})\) exhibited its greatest impact on grasslands, followed by bare or sparsely vegetated areas, though its mean values remained substantially lower than \(\Delta SA_{GPP}(SA_{RSM}\mid SAP_{PRE})\). RSM showed greater influence on GPP in bare soil and sparsely vegetated areas than in other  
Figure 6: Decoupling the effects of RSM and PRE on GPP. (**a**) Summer mean standardized anomalies of RSM and GPP, grouped by PRE standardized anomalies. Circles indicate the mean GPP-standardized anomalies within each PRE anomaly bin; (**b**) summer mean standardized anomalies of PRE and GPP, grouped by RSM standardized anomalies. Circles represent the mean GPP-standardized anomalies within each RSM anomaly bin, (**c,d**) boxplot distributions of GPP-standardized anomalies corresponding to different RSM and PRE anomaly conditions. Red squares denote the respective mean values, while circles show individual GPP-standardized anomaly values for each condition. ecosystems. This was likely due to the harsh environmental conditions in these regions, where precipitation was extremely limited (Supplementary Figure S1) and vegetation mainly relied on RSM as its primary water source. These results indicate that while elevated \(SA_{PRE}\) could enhance GPP growth, its promoting effect was consistently weaker than that of \(SA_{RSM}\). A parallel analysis employing the MODIS GPP dataset corroborated these pixel-level RSM-PRE effects on GPP, yielding comparable results (Figure S4). Figure 7: The influence of RSM and PRE on ecosystem productivity in the study area, where panels (**a**,**c**,**e**), display the spatial distribution of GPP under high-RSM conditions (\(\Delta SA_{GPP}(SA_{RSM}\,|SA_{PRE})\)), high-PRE conditions (\(\Delta SA_{GPP}(SA_{PRE}\,|SA_{RSM})\)), and their absolute difference (\(\Delta SA_{GPP}(\Delta SA_{RSM}\,|SA_{PRE})-\Delta SA_{GPP}(SA_{PRE}\,|SA_{RSM})\)), while panels (**b**,**d**,**f**), reveal the effects of RSM and PRE on GPP across different vegetation types.