## 2 Experiment Description  
### Study Area and Data Used  
The Loess Plateau extends from 33\({}^{\circ}\) 41\({}^{\prime}\) N to 41\({}^{\circ}\) 16\({}^{\prime}\) N in latitude and from 100\({}^{\circ}\) 52\({}^{\prime}\) E to 114\({}^{\circ}\) 33\({}^{\prime}\) E in longitude. Given its low vegetation coverage, the ecology of Loess Plateau is very fragile, and the soil erosion is severe [27]. The Loess Plateau features a temperate continental monsoon climate, wherein rainfall is concentrated in July-September. In this paper, the Loess Plateau is divided into 10 subregions (Figure 1) based on differences in soil type and topography (Table 1). This geographical zoning map of the Loess Plateau was acquired from the Loess Plateau Scientific Data Center ([[http://loess.data.ac.cn](http://loess.data.ac.cn)]([http://loess.data.ac.cn](http://loess.data.ac.cn))). Considering that areas IX and X do not include the selected meteorological stations (right panel of Figure 1), only the eight remaining areas are discussed in this paper. Extreme climatic indices can be calculated using the Climate Dex package developed by R Language (RClimDex) model, which was developed by the Climate Research Department, Meteorology Office, Canada, and can be downloaded from [[http://etccdi.pacificclimate.org/software.shtml](http://etccdi.pacificclimate.org/software.shtml)]([http://etccdi.pacificclimate.org/software.shtml](http://etccdi.pacificclimate.org/software.shtml)). The RClimDex model is driven by daily precipitation and minimum and maximum temperatures, producing 27 indices that can be used to measure extreme weather conditions. A total of 16 extreme temperatures and 11 extreme precipitation indices are available (see Table 2). The input meteorological parameters of 83 stations were obtained from the China Meteorological Administration ([[http://data.cma.cn/](http://data.cma.cn/)]([http://data.cma.cn/](http://data.cma.cn/))). PWV can be derived through many techniques, such as sounding measurement, very long baseline interferometry, microwave radiation, global navigation satellite system, and reanalysis data [28]. In this study, we used PWV data from the European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis ([[https://www.ecmwf.int/](https://www.ecmwf.int/)]([https://www.ecmwf.int/](https://www.ecmwf.int/))). This product assimilates multi-source data, such as radiosonde data, satellite radiation data, and satellite altimetry data [29]. The temporal and spatial resolutions of the PWV product used were four times a day and \(0.125^{\circ}\times 0.125^{\circ}\), respectively. The PWV values of the meteorological stations were interpolated using the values of the four surrounding grid points derived from ECMWF data. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**No.** & **Geographical Area.** & **No.** & **Geographical Area.** \\ \hline I & River Impact Plain & VI & Loess Hills in the \\ II & Loess Tableland & VII & Wind Dunes \\ III & Loessian Rolling Hills & VIII & Stone Mountain \\ IV & Loess Beam Hills & IX & The Sandy Loess Hills \\ V & Loess Wide Valley & X & Rocky Hills \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Loess Plateau subdivisions. Figure 1: Geographical zoning (Cf. Table 1) map of the Loess Plateau and the distribution of meteorological stations. A long-term NDVI time series for the study region was obtained from the Global Inventory Modeling and Mapping Studies (GIMMS). We downloaded the 15-day composite NDVI3g dataset with a spatial resolution of 8 km over the 35 years of 1981-2015 ([[https://ecocast.arc.nasa.gov/data/pub/gimms/3g.v1](https://ecocast.arc.nasa.gov/data/pub/gimms/3g.v1)]([https://ecocast.arc.nasa.gov/data/pub/gimms/3g.v1](https://ecocast.arc.nasa.gov/data/pub/gimms/3g.v1))). This dataset has been processed such that radiation and geometric correction were applied to the World Geodetic System 1984(WGS84) coordinate system [30]. The maximum value composite method was adopted to obtain the monthly NDVI time series over Loess Plateau.