## 4 Discussion and Conclusions
The Thornthwaite equation, which was easy to calculate, was used to calculate the potential evapotranspiration in this study. Although the calculation of the PET was to obtain a relative temporal estimation [18], the use of simple or complex methods to calculate the PET provided similar results when a drought index was calculated [57]. However, a study has shown that the Penman-Monteith (PM) equation produced better results when used in places with climate changes, especially in the arid regions of China [58]. With such a focus on the northwest region in this study, a more accurate drought assessment may be obtained by using the PM equation. In addition, from the results shown in Figures 5 and 6, even in areas of distribution parameter change in one class (e.g., (1,0)), the under-estimation or over-estimation of the current SPEI index remains a geographic uncertainty, which may be the result of the trend effects of the distribution parameter itself. Therefore, further research may use a more accurate calculation method of potential evapotranspiration and consider the influence of the trend itself on the distribution parameters. The main findings of this study are as follows:  
1. During the period 1979-2020, both precipitation and evapotranspiration showed an increasing trend. The results of the D series showed that a drier tendency in Northwest China can be found;
2. Northwest China (CRB) was the main nonstationary hotspot response areas with changes in distribution parameters;
3. The stationary SPEI incorrectly identified more severe droughs as less severe, which caused the under-estimation of the severity of droughs in a changing climate;
4. Drought warnings should focus on Northwest and Southwest China; the former and latter were under-estimated in the spring-summer and the fall-winter seasons, respectively, under the current stationary drought index assessment system. In conclusion, this study provides support for drought assessment and early warning systems that consider non-stationarity under climate change. **Supplementary Materials:** The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/app12178479/s1](https://www.mdpi.com/article/10.3390/app12178479/s1)]([https://www.mdpi.com/article/10.3390/app12178479/s1](https://www.mdpi.com/article/10.3390/app12178479/s1)), Figure S1: Long-term trends of precipitation for each month during the period 1979-2020, Figure S2: Long-term trends of evapotranspiration for each month during the period 1979-2020. **Author Contributions:** Conceptualization, resources, formal analysis, writing--original draft, D.W.; methodology, writing--review and editing, H.-C.Y. ; and conceptualization, methodology, writing--review and editing, J.-H.L. and J.-S.K. All authors have read and agreed to the published version of the manuscript. **Funding:** We appreciate the support of the State Key Laboratory of Water Resources and Hydropower Engineering Science, at Wuhan University. This work was also supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. 2021R1A2C1013190) and supported by a grant(2022-MOIS63-001) of Cooperative Research Method and Safety Management Technology in National Disaster funded by Ministry of Interior and Safety (MOIS, Korea). **Data Availability Statement:** All data, models, and code generated or used during the study appear in the published article. **Conflicts of Interest:** The authors declare no conflict of interest.