## 4 Results
### Correlation between the EQI and Extreme Climate Indices  
The correlation coefficient between vegetation and extreme climate indices can serve as an indicator for evaluating the influence of extreme climatic conditions upon vegetation growth. The inter-annual correlation coefficient is exhibited in Figure 5 as a spatial distribution pattern. In the extreme temperature indices, DTR, TN90P, TX90P, TNn, SU25, and EQI are mainly negatively correlated. Specifically, the DTR and SU25 show a negative correlation in more than 80% of the regions studied. A prominent negative correlation is demonstrated in nearly 19.5% and 11.21% of these regions; it largely diffuses in western regions of Aksu, Kashgar, and Hotan. The correlation between the TN10P, TX10P, and EQI is predominantly positive, while the region displaying a significantly positive correlation with the TX10P encompasses 11.60% of the overall region. It is chiefly distributed in Western Aksu, Kashgar, and Western Hotan. The TXx positively correlated areas are chiefly distributed in the southern margin of the basin. The positive correlation between extreme precipitation indices SDII, RX1d, and RX5d and the EQI occupies more than 60% of the regions, and 6.73%, 10.75%, and 11.81% of regions exhibit significant positive correlations, respectively. SDII, RX1d, and RX5d show significant positive correlations with the EQI,  
Figure 4: Change trend of extreme climate indices in the Tarim Basin during 2000–2022. primarily concentrated in the western regions of Kashbar and Hotan. The regions with a negative correlation between the CDD and EQI represented 75.12%; the areas with a prominent negative correlation were primarily observed in the northern margin of the basin and the eastern region of Ruoqiang County. Figure 5: _Cont._  
Figure 6 displays the spatial distribution pattern of intra-annual correlation coefficients between the EQI and extreme climate indices. The correlations of the extreme temperature indices, the TX90P, TN10P, TX10P, and TN90P, with the EQI are analogous to the distributing pattern of inter-annual correlation coefficients. The regions where TN10P, TX10P, and EQI are positively correlated are distributed in most parts of the Tarim Basin, but the areas where TN10P is significantly positively correlated are relatively few, mainly concentrated in Moyu County. The areas where TX10P is significantly negatively correlated are greater than the significantly positively correlated areas, mainly distributed on the northern edge of the Tarim Basin, where the EQI is higher. Conversely, TX10P shows a significant positive correlation in the areas with higher EQI at the southern edge of the Tarim Basin. TN90P, TX90P, and EQI are negatively correlated in most parts of the Tarim Basin. The areas where TX90P is significantly negatively correlated account for 9.15%, but these areas are concentrated in the regions with lower EQI values in the southern part of Kashgar and the western part of Hotan. TNn, TXx, RX1d, and RX5d are significantly positively correlated in areas with high EQI values, while areas with sparse or no vegetation show a negative correlation. In particular, the regions where TNn and TXx are significantly negatively correlated are close to 80%, indicating a clear regional concentration distribution feature. The spatial distribution pattern of intra-annual correlation coefficients between the DTR and EQI exhibits a significant divergence from that of the inter-annual correlation coefficients. In terms of inter-annual analysis, DTR was negatively correlated with the EQI in most regions. Conversely, on the intra-annual scale, the positive correlation area accounted for 78.68%. This positive correlation was primarily observed in regions characterized by sparse vegetation. Conversely, oasis areas with high EQI values displayed a significant negative correlation. It is evident that, in contrast to the inter-annual scale, the correlation on the intra-annual scale exhibits distinct regional concentrations.