## 4 Discussion  
### Validation of Meteorological Interpolation Data  
The accuracy of meteorological data seriously affects the results of drought event assessment. We compared the interpolation data with the European Centre for Medium-Range Weather Forecasts Reanalysis 5 (ERA5, [[https://cds.climate.copernicus.eu/#](https://cds.climate.copernicus.eu/#)]([https://cds.climate.copernicus.eu/#](https://cds.climate.copernicus.eu/#))!/home (accessed on 30 July 2022)) at monthly time-steps for the same period for verification. The datasets from the ERA5 potentially supersede other reanalysis products due to their high spatial and temporal resolution, and they have been widely used in meteorology-related research [51; 52]. We analyzed the performance of the dataset across different climate zones according to new and improved current (1980-2016) Koppen-Geiger classifications (Figure 11). The classification is based on the threshold values and seasonality of monthly air temperature and precipitation, which can be used for studies based on differences in climatic regimes [53]. We selected climate classifications with an area greater than 8% of the total area (BWk, ET, BSk, Cfa, Dwa, Cwa, and Dwb) for analysis. Together, these classifications account for more than 90% of the total area. The selected Koppen-Geiger classifications as listed in Table 3.  
The verification results of temperature, precipitation, and relative humidity data are shown in Figures 12-14, respectively. The interpolation data were in good agreement with the ERA5 data, with most \(R^{2}\) greater than 0.90. The \(R^{2}\) of temperature data for climate classifications in China were all equal to 0.99, while precipitation data were all \(\geq\) 0.94, and relative humidity data were all \(\geq\) 0.72. In general, the interpolation data met the data accuracy requirements. showed a large area of the Qinghai-Tibet Plateau region with a drying trend in summer and autumn, while the VPD showed a more significant drying trend in autumn. Notably, in the future, projections point to the increasing risk of drought in southwest China and the Qinghai-Tibet Plateau, with the risk being almost twice as high as that for the rest of China [59]. Figure 12: Comparison and verification of interpolation data and the European Centre for Medium-Range Weather Forecasts Reanalysis 5 (ERA5) data of temperature for selected Köppen–Geiger classifications (**a**) BWk, (**b**) ET, (**c**) BSk, (**d**) Cfa, (**e**) Dwa, (**f**) Cva, (**g**) Dwb, and (**h**) across China (see Table 3 for explanations of the abbreviations). \begin{table}
\begin{tabular}{c c c} \hline \hline
**Köppen–Geiger Classifications** & **Abbreviations** & **Area Proportions (\%)** \\ \hline Arid, desert, cold & BWk & 21.806 \\ Polar, tundra & ET & 15.098 \\ Arid, steppe, cold & BSk & 12.792 \\ Temperate, no dry season, hot summer & Cfa & 11.570 \\ Cold, dry winter, hot summer & Dwa & 11.344 \\ Temperate, dry winter, hot summer & Cwa & 9.271 \\ Cold, dry winter, warm summer & Dwb & 8.545 \\ Cold, dry winter, cold summer & Dwc & 5.602 \\ Temperate, dry winter, warm summer & Cwb & 2.937 \\ Tropical, savannah & Aw & 0.293 \\ Polar, frost & EF & 0.264 \\ Cold, no dry season, cold summer & Dfc & 0.202 \\ Temperate, no dry season, warm summer & Cfb & 0.082 \\ Tropical, monsoon & Am & 0.073 \\ Cold, no dry season, warm summer & Dfb & 0.033 \\ Cold, no dry season, hot summer & Dfa & 0.028 \\ Cold, dry summer, cold summer & Dsc & 0.021 \\ Arid, steppe, hot & BSh & 0.014 \\ Tropical, rainforest & Af & 0.013 \\ Cold, dry summer, warm summer & Dsb & 0.012 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Köppen–Geiger classifications and their area proportions of China (classifications with area proportions greater than 8% were selected for further analysis).