## 2 Materials and Methods
### Methods
#### 2.3.4 Indices of Climatic Extremes  
To further analyze the relationship between temperature, precipitation, and LDDI, this study selected 24 extreme climate indices recommended by ETCCDI ([PERSON] et al., 2018; [PERSON] et al., 2018; [PERSON] et al., 2018) to describe extreme temperature and precipitation. Table 2 shows the selected extreme climate indices, which can describe the specific frequency, amplitude, and duration of extreme precipitation and temperature events in the study area, such as the maximum 1-day precipitation (Rx1 day), the number of heavy precipitation days (R25 mm), and summer days (SU) (days with maximum temperature exceeding 25 \({}^{\circ}\)C). Firstly, 14 temperature extremes and 10 precipitation extremes indices are calculated from the CN05 observation dataset based on Table 2. Then, the M-K test and Theil-Sen slope are used to analyze the variation trend of the selected extreme climate indices from 1982 to 2015. Finally, the Pearson correlation coefficient is used to analyze the relationship between LDDI and extreme climate indices.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Slope (\(LDDI_{\text{obs}}\)) a**} & \multirow{2}{*}{**Driving Factors**} & \multicolumn{2}{c}{**Standard of Calculation**} & \multicolumn{2}{c}{**Contribution Rate**} \\ \cline{3-6}  & & \multicolumn{1}{c}{_Slope (\(LDDI_{\text{CC}}\)) b_} & \multicolumn{1}{c}{_Slope (\(LDDI_{\text{HA}}\)) c_} & **Climate (CC)** & **Human (HA)** \\ \hline \multirow{3}{*}{\(>\)0} & CC\&HA & \(>\)0 & \(>\)0 & \(\frac{slope(LDDI_{\text{LC}})}{slope(LDDI_{\text{obs}})}\) \\  & CC & \(>\)0 & \(<\)0 & 100 & 0 \\  & HA & \(<\)0 & \(>\)0 & 0 & 100 \\  & CC\&HA & \(<\)0 & \(<\)0 & \(\frac{slope(LDDI_{\text{LC}})}{slope(LDDI_{\text{obs}})}\) \\ \(<\)0 & CC & \(<\)0 & \(>\)0 & 100 & 0 \\  & HA & \(>\)0 & \(<\)0 & 0 & 100 \\ \hline \hline \end{tabular}  
* Notes: “a” represents the slope of LDDI from remote sensing, “b” represents the slope of LDDI from binary regression predictions, and “c” represents the slope of the \(LDDI_{\text{HA}}\). All slopes were calculated using linear trend method.  
\end{table}
Table 1: Calculation criteria for relative contribution rate of driving factors in LDD process changes.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**ID** & **Index** & **Definition** & **Unit** \\ \hline
1 & FD a & Annual count when TN(daily minimum) \(<\) 0 \({}^{\circ}\)C & days \\
2 & SU25 a & Annual count when TX(daily maximum) \(>\) 25 \({}^{\circ}\)C & days \\
3 & TR20 a & Annual count when TN(daily minimum) \(>\) 20 \({}^{\circ}\)C & days \\
4 & TXa a & Monthly maximum value of daily maximum temperature & \({}^{\circ}\)C \\
5 & TNx a & Monthly maximum value of daily minimum temperature & \({}^{\circ}\)C \\
6 & TXn a & Monthly minimum value of daily maximum temperature & \({}^{\circ}\)C \\
7 & TNn a & Monthly minimum value of daily minimum temperature & \({}^{\circ}\)C \\
8 & TN10p a & Percentage of days when TN \(<\) 10 th percentile & days \\
9 & TX10p a & Percentage of days when TX \(<\) 10 th percentile & days \\
10 & TN90p a & Percentage of days when TN \(>\) 90 th percentile & days \\
11 & TX90p a & Percentage of days when TX \(>\) 90 th percentile & days \\
12 & WSDI a & Annual count of days with at least 6 consecutive days when TX \(>\) 90 th percentile & days \\
13 & CSDI a & Annual count of days with at least 6 consecutive days when TN \(<\) 10 th percentileFD & days \\
14 & DTR a & Monthly mean difference between TX and TN & \({}^{\circ}\)C \\
15 & RX1 day b & Monthly maximum 1-day precipitation & mm \\
16 & Rx5 day b & Monthly maximum consecutive 5-day precipitation & mm \\ \hline \hline \end{tabular}
\end{table}
Table 2: Extreme climate indices used in this study.