## 4 Discussion
### Impacts of Climate Indices on the Ecosystem  
To determine the relative sensitivities of the ecosystem indicators to the climate indices, stepwise multiple linear regressions between the 21 climate indices and the three ecosystem variables are carried out at meteorological scales in the study area (see Section 3.4). Generally, the ecosystem variables are more sensitive to temperature indices than precipitation indices (Table 3). [PERSON] et al. [34] also found that mean annual air temperature exerts a stronger effect on the ecosystem than annual total precipitation in Hubei Province over the past decades based on correlation analyses between climatic parameters and ecological indicators. With a new GPP data developed by a machine learning algorithm, the results from [PERSON] et al. [35] reveal that the GPP in Hubei Province is more sensitive to temperature than precipitation and radiation over 1982-2015. This could be explained by the abundant annual precipitation in the study area that causes the regions to not be water deficient for most years [38]. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Indices** & **MEI** & **PDO** & **AMO** \\ \hline SU25 & \(0.158\) & \(-0.054\) & \(0.372\) ** \\ ID0 & \(-0.061\) & \(-0.148\) & \(-0.040\) \\ TR20 & \(-0.259\) * & \(-0.247\) * & \(0.408\) ** \\ FD0 & \(-0.252\) * & \(-0.085\) & \(-0.012\) \\ SU35 & \(-0.232\) * & \(-0.356\) ** & \(0.339\) * \\ GSL & \(0.055\) & \(0.008\) & \(0.060\) \\ TN10p & \(-0.190\) & \(-0.071\) & \(-0.108\) \\ TX10p & \(0.242\) * & \(0.231\) * & \(-0.040\) \\ TN90p & \(-0.046\) & \(-0.126\) & \(0.265\) * \\ TX90p & \(0.120\) & \(0.052\) & \(0.067\) \\ WSDI & \(0.078\) & \(-0.066\) & \(0.529\) ** \\ CSDI & \(0.072\) & \(-0.041\) & \(-0.184\) \\ SDII & \(0.118\) & \(0.118\) & \(0.304\) \\ R10 & \(0.147\) & \(0.190\) & \(0.044\) \\ R20 & \(0.118\) & \(0.141\) & \(0.168\) \\ R50 & \(0.072\) & \(0.099\) & \(0.220\) \\ CDD & \(0.039\) & \(0.009\) & \(-0.091\) \\ CWD & \(-0.046\) & \(0.111\) & \(-0.096\) \\ R95p & \(0.067\) & \(0.084\) & \(0.276\) * \\ R99p & \(0.017\) & \(-0.010\) & \(0.310\) * \\ PRCPTOT & \(0.111\) & \(0.144\) & \(0.147\) \\ \hline \hline \end{tabular}  
* \(p<0.1\) and ** \(p<0.01\).  
\end{table} 
Table 4: Correlation coefficients between climatic indicators for the whole study area and circulation modes of climate variability during 1961–2015. The relationships between the three ecosystem variables and the six most sensitive climate factors were built using MLR to further quantify the impacts of the selected indices on interannual variations in the ecosystem for Hubei Province as a whole (Figure 5). To show the spatial sensitivity of the ecosystem to the six indices, spatial patterns of the ecological differences between years with the five highest values and years with the five lowest values for each climate index are calculated (Figure 6). The result of MLR is consistent between NDVI and GPP; both ecosystem variables are most vulnerable to FD0 and R50. In addition, WSDI has the most negative correlations with LAI. Figure 6 further indicates that FD0 and CDD exert the most negative impacts on most regions. In summary, the results reveal the negative effects of FD0, WSDI, R50, and CDD on the ecosystem in Hubei Province integrally or regionally. Based on tree rings and land surface simulations for the 20 th century, [PERSON] et al. [68] also found that hot extremes and dry extremes can trigger exceptional changes in growth or mortality rates of vegetation, which often result in reductions in ecosystem productivity. Although the negative impacts caused by hot extremes and dry extremes on vegetation indices and ecosystem productivity are frequently diagnosed and studied for many places around the world [23; 69; 25], the potential roles of cold extremes and extreme precipitation are reported less. In addition, our statistical analyses reveal that FD0 and R50 could also reduce ecosystem productivity in Hubei Province.