## 4 Discussion
### Uncertainties and Perspectives  
Uncertainties may still exist when analyzing the relation between ecosystems and climate extremes, as well as the relation between climate extremes and large-scale oceanic and atmospheric circulation oscillations. Since the climate datasets and the method to extract the climate extremes are robust, the uncertainties may firstly result from the ecosystem variables. For example, although some improvements have been made from an increase in eddy flux sites and updated climate data, large uncertainties still exist in GPP data ([PERSON] et al., 2015). Secondly, different resolutions between ground observation and satellite-derived data might introduce uncertainties in the correlation analysis between climate and vegetation indices at the site scale. [PERSON] et al. (2016) indicated that there is a bias between in situ data and satellite-derived pixel average in a heterogeneous area. Thirdly, other factors may also influence ecosystems, such as CO\({}_{2}\) fertilization effects ([PERSON] et al., 2016), nitrogen deposition and interference from human activities (e.g., land cover change) ([PERSON] et al., 2015). However, these factors are more likely to cause long-term trends in ecosystem variables, which are beyond the scope of this study. In addition, the removal of linear trend and the performance of MLR with a temporal moving window can further eliminate the effects from these long-term factors and enhance the climate-induced interannual variations in the ecosystem variables to some extent. Nevertheless, the ecological response to climate extremes at larger spatial scales, combined with other factors, should be comprehensively investigated in the future  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Variables** & **SU25** & **FD0** & **WSDI** & **SDII** & **R50** & **CDD** \\ \hline LAI & 27.8 * & 22.1 & 16.6 & 17.5 & 6.4 & 9.6 \\ NDVI & 12.9 & 11.2 & 3.3 & 41.1 * & 29.4 & 2.1 \\ GPP & 32.3 * & 13.4 & 18.7 & 17.2 & 10 & 8.4 \\ \hline \hline \end{tabular}  
* The maximum values of the contribution rate for each ecosystem variable are marked.  
\end{table}
Table 5: Relative contribution rates (%) of the six climate indices to ecosystem variables in Hubei Province.  
Figure 7: Temporal evolution of the relative contribution rate of the six climate indices to (**a**) LAI, (**b**) NDVI, and (**c**) GPP, respectively.  
studies. Finally, we have also found several discrepancies between climate extremes and atmospheric and oceanic climate indices at annual time scale. For instance, the precipitation extremes in 1998 in the study area, which are shown by R50, R95p, and R99p extreme precipitation indices, correspond to the high values of MEI and PDO in 1997. In fact, comprehensive analyses between consecutive days with precipitation extremes in China and oceanic and atmospheric climate indices have also not found expected connections [4]. This possibly indicates a monthly delayed responses of regional climate extremes to large-scale oceanic and atmospheric circulation oscillations (e.g., MEI), which could not be captured by the yearly data. We leave this delayed response of climate extremes for a further study.