## 4 Discussion  
### Impacts of Climate Factors on the LDD Processes  
This study used multiple linear regression to investigate the impact of climate factors (temperature and precipitation) on the LDD process at the pixel scale in coastal areas. Most areas, except for the northern coastal areas, exhibited a non-significant positive relationship between precipitation and temperature and the LDD process, which is consistent with previous studies [12]. The northern coastal region is dominated by cropland, while the southern coastal region is dominated by forest, which accounts for 84.5% of the total area. Therefore, changes in rainfall significantly impact the growth of natural vegetation and crops in these regions due to moisture conditions. The significance level of the multiple regression model indicates that the relationship between the LDDI in the northern coastal region and climate factors is significant at the pixel scale (1 km \(\times\) 1 km), indicating that this region is more vulnerable to the effects of climate change (Figure 3c). By analyzing the Pearson correlation between region-average LDDI and extreme temperature indices, significant negative correlations were found between LDDI and FD, TN10p, TX10p, CSDI, and DTR. On the other hand, significant positive correlations were observed between LDDI and TR, TN90p, and TX90p. Except for DTR and TX90p, all other extreme temperature indices show a significant relationship with LDDI, passing the significance test at 0.01 (Table S4). Among the eight extreme temperature indices that display a significant relationship with LDDI, TN10p and TN90p reign supreme with the highest Pearson correlation coefficients with LDDI, reaching \(-\)0.63 and 0.63, respectively. The next most remarkable correlation is with FD, boasting a Pearson correlation coefficient of \(-\)0.52 with LDDI. The Pearson correlation coefficient between DTR and LDDI is the smallest, with a value of \(-\)0.35. Moreover, LDDI displays a significant relationship with some extreme precipitation indices, such as SDII, R10, and R95p, which were significantly positively correlated (Table S5). Among all extreme precipitation indices that exhibit a significant relationship with LDDI, R10 stands tall with the highest Pearson correlation coefficient with LDDI at 0.46, followed by R95p, which holds a Pearson correlation coefficient of 0.44 with LDDI. The smallest correlation is with SDII, displaying a Pearson correlation coefficient of 0.41. Figure 6 shows significant trends in the most extreme temperature and precipitation indices in China's coastal region from 1982 to 2015. Overall, the intensity and frequency of extreme climatic events in China's coastal region have increased. The trend in temperature change is particularly noticeable, as nearly all extreme temperature indices, except for TXx, showed a significant upward trend at the 0.01 or 0.05 significance level. Combining the Pearson correlation coefficients between LDDI and extreme climate (temperature and precipitation) indices, it is evident that climate warming has a significant positive impact on LDDI. Moreover, the increase in precipitation intensity also has a significant positive impact on LDDI. It is widely accepted that global warming significantly impacts terrestrial ecosystems. Climate change can worsen land degradation through higher temperatures, evaporation rates, intense precipitation, drought, frequent floods, and increased soil salinity [56; 57]. For example, extreme drought increases vegetation thermal and water stress, limiting the growth of natural vegetation and crops and leading to the degradation of farmlands, grasslands, and forests [4; 58]. Additionally, climate change can exacerbate soil erosion, intensifying land degradation. The north of the coastal regions comprises many croplands, with their surface soil easily threatened by wind and water erosion [4; 58]. High temperatures and strong winds caused by climate change dry the soil and exacerbate wind erosion [59].