## 3 Results
### Climatic Effects on Shifting the Drought Impacts  
The spatial variations of the NDVI-PDSI regression slopes of the four natural vegetation types against the normal temperature and precipitation gradients suggest that all the regression slopes increased in geographic regions with higher temperatures but remained stable across geographic precipitation gradients (Figure 11). In other words, natural vegetation in the warmer regions of the study area were more sensitive to drought, as indicated by their larger NDVI-PDSI regression slopes. However, the spatial precipitation patterns did not demonstrate a similar geographic influence. Additionally, the vegetation in developed lands seems to be less affected by temperature or precipitation changes (Figure 11). Figure 10: Interquartile ranges (IQR) of the regression slopes and \(p\)-values between NDVI-PDSI regression slopes and vegetation types. b0: intercept; chap: chaparral; CSS: coastal sage scrub; grass: grassland. The dummy regression intercepts (b0) refer to the NDVI-PDSI regression slopes for the reference group (developed land), while the regression slopes (forest, chap, CSS, and grass) of the dummy regressions refer to the relative changes in NDVI-PDSI regression slopes of these vegetation types, compared to developed-land vegetation. The bootstrapped regressions indicate that the standardized slopes (beta) against precipitation were smaller than those of temperature for all the major vegetation types (Figure 12). We calculated the variance inflation factor (VIF) for all the multiple linear regressions (Figure S1). All the VIFs were \(<\)1.5, and thus these regressions did not violate the assumption of no multicollinearity [89]. The beta values of precipitation mainly ranged between \(-\)0.3 and 0.3, which denotes that one SD (standard deviation) precipitation change would only induce \(<\)0.3 SD change of the regression slopes between NDVI anomalies and PDSI. In contrast, a 1 SD temperature increase would raise the NDVI-PDSI  
Figure 11: Quartile statistics of the regression slopes between NDVI anomalies and PDSI along the gradients of normal temperature (**left**) and precipitation (**right**) for chaparral (1 st panel), coastal sag scrub (2 nd panel), grassland (3 rd panel), forest (4 th panel), and developed land (5 th panel). NDVI-PDSI regression slopes increase with temperature gradients, but are stable as precipitation changes. regression slopes by up to 0.5 SD. Additionally, precipitation showed opposite effects in changing the vegetation sensitivity between the wet and dry seasons. In other words, more precipitation could decrease the NDVI-PDSI slopes in winter and spring, and increase them in summer and fall, resulting in insignificant annual effects (\(p>0.05\)) for most vegetation communities, except for grassland (Figure 12). Temperature demonstrated significant (\(p<0.05\)) and positive beta values in nearly all bootstrapped regressions (seasonal and annual), except for grassland in spring and summer, as well as developed land in all seasons. Thus, warmer temperatures consistently increased the natural vegetation sensitivity to drought, and the effects were prone to be greater in the dry season. Strikingly, the chaparral communities displayed very high median standardized slopes of temperature (\(>\)0.3 SD, \(p<0.05\)) in all seasons (Figure 12e). Figure 12: IQR of the regression coefficients (upper), \(p\)-values (middle), and standardized coefficients (beta) (lower) between the NDVI-PDSI regression slopes and normal temperature (**left**) or normal precipitation (**right**). X axes: ANL: annual; DJF: winter; MAM: spring; JJA: summer; SON: fall. Note that the scales of the Y axes for temperature (**left**) and precipitation (**right**) results are different. Regression coefficients (upper) indicate the changes in NDVI–PDSI regression slopes per 1 \({}^{\circ}\)C temperature increase (**a**) or per 100 mm precipitation increase (**b**).