## 4 Discussion  
Our analysis reveals the relative sensitivity of five major wildland vegetation types and developed areas to increased warming and aridity in Southern California, a Mediterranean climate region. Climatic water deficit (CWD), which combines the influences of temperature and precipitation, has been shown to be an important driver of woody plant decline and mortality in drought [94], which is consistent with the results presented here. [PERSON] and [PERSON] [95] recently investigated the impacts of changing temperature and precipitation on global NDVI. Here, we further focus on how a changing climate will affect vegetation drought sensitivity. The bootstrapped multiple regression model utilized in our study dissociated the effects of temperature and precipitation on drought stress, and allowed for examination of the seasonal variations of these effects. The bootstrapping approach successfully reduced the overestimation of minuscule effects, which generally occurs in big data (e.g., remotely sensed data) analytics [90]. Precipitation consistently showed more insignificant results in predicting vegetation sensitivity to drought, while temperature displayed significant effects in nearly all bootstrapped regressions. Previously, [23] has found that the forests in southwest U.S. are particularly sensitive to the increasing aridity and warmth. We further compared the differential responses of drought sensitivity (NDVI-PDSI regression slopes) to temperature and precipitation changes in forest, shrubs, grass, and developed land. All the natural vegetation types showed greater sensitivity in the warmer areas of the region, implying that temperature increases alone will be an important determinant of vegetation vulnerability to global warming in the 21 st century. Vegetation in developed lands were consistently less responsive to warming, indicating the effects of irrigation in droughts, although winter precipitation increases seem to be able to reduce drought sensitivity in urban vegetation (Figure 12f). grasslands showed less sensitivity in response to the combined effects of high temperatures and drought stress; while chaparral displayed very high sensitivity in all seasons. Thus, grassland and chaparral seem to be the most resistant and vulnerable communities, respectively, to drought under the effects of a warmer climate (Figure 12e). This is consistent with a recent study by [96], who concluded that forest and grass displayed longer and shorter drought legacy effects, respectively, but both showed greater drought resilience than shrubs. These differences arise from different eco-hydrological and physiological responses of these plant's functional groups (e.g., rooting systems, water-use patterns, and differential hydraulic responses). It should be mentioned that uncertainties exist in the relationship between extreme events in climate and vegetation (e.g., non-linearities and time lags), but pixels having more drought stress should have experienced more vegetation extremes and, therefore, the slopes of the NDVI-PDSI linear regressions could be used as an indicator of apparent sensitivity [51]. Another limitation of this study is related to the inconsistency of the databases used, in terms of spatial resolution. Bilinear interpolation was used in rescaling the PDSI and PRISM climate normals to MODIS resolution. For these continuous variables, bilinear interpolation can provide more accurate predictions than the other simple resampling approaches, such as nearest neighbor interpolation, and it has been widely used to rescale climate variables in similar studies, e.g., [97; 98; 100]. Some researchers have developed statistical or dynamical downscaling techniques for climate variables, e.g., [101; 102]. However, as the space-time variability and patterns of a climate variable are highly dependent on the location of this variable, it is not practical to directly apply a downscaling approach developed for another region to our study region, or to use a downscaling approach for all climate variables.