## 5 Summary  
In this paper we conducted an intensive analysis on spatiotemporal mode characteristics of TCWV in China during a recent 20-year period (2002-2022) based on the AIRS dataset and, furthermore, the trend of monthly mean time series over one entire year was predicted by using lows. 1. The annual and seasonal distribution of TCWV in China are roughly the same, and have obvious longitude and latitude distribution characteristics. That is, the variation trend of TCWV in China with longitude shows a \"V\" shape as a whole and the TCWV in China decreases with the increase of latitude. The spatial distribution of TCW in  
Figure 12: The relationship between TCWV and SST. (**a**) Correlation of TCW with SST. (**b**) Distribution of SST in China. China has an obvious southeast-northwest direction. Generally, the seasonal variation of the TCWV in the same area is summer > autumn > spring > winter. 2. By performing EOF decomposition of the TCWV in China, the contribution rate of variance of the first mode is 31.47%, indicating that it can reflect the typical spatial distribution pattern of the TCWV in China, that is, the positive distribution from southeast to northwest. 3. The TCWV in China showed an overall growth trend, and the M-K mutation test found that there was a significant mutation after 2014. After 2017, the UF value was greater than 1.96, and the upward trend was more obvious. The monthly variation curve shows a slightly positive deviation of the 'bell-shaped' curve, while the four seasons M-K curve shows that each has different mutation points. 4. Using the SARIMA model, considering the trend and seasonality of TCWV time series, the optimal model is obtained. The average absolute error percentage (MAPE), mean square error (MSE), and R2-score are 2.65%, 0.3229 and 0.9949, respectively. In this study, the spatiotemporal mode characteristics of TCWV were analyzed in China. These results validate the applicability and reliability of TCWV studies using the EOF, the Mann-Kendall test method. An increasing trend of TCWV in China has been observed from the analysis of the AIRS dataset. It has been illustrated that the upward trend of TCWV is becoming more apparent in recent years, which is likely due to various factors including anthropogenic global warming, large-scale moisture convergence, and dynamics. This paper includes a detailed study and analysis of SARIMA in forecasting time series data. The time model SARIMA can reflect the linear characteristics of time series data, and the model adds seasonality to the ARIMA model. Therefore, it can fit and predict the TCWV variation relatively accurately because of the added influence of seasonal factors on TCW variation. These results provide a reference for understanding the characteristics of the global water cycle. S.S.: investigation and Writing--original draft. H.L. : Methodology and resources, Writing--Review and editing, funding acquisition and validation. Y.W. : Software and validation. C.T. : Conceptualization and Supervision. All authors have read and agreed to the published version of the manuscript. This study is supported by the Provincial Natural Foundation of Anhui Province (No. 1808085 QF197), the scientific research start-up fund for high-level introduced talents of Anhui University of Science and Technology (No. 13190007), the University Natural Science Research Project of Anhui Province of China (No. KJ2019A0103), the Specialized Research Fund for State Key Laboratories (No. 201909), National key research and development program (No. 2017 YFD0700501). Not applicable. We would like to thank AQUA-AIRS team for providing the necessary datasets used in this study. The version 6.0 level 3 dataset of AIRS are downloaded from [[https://disc.gsfc.nasa.gov/datasets](https://disc.gsfc.nasa.gov/datasets)]([https://disc.gsfc.nasa.gov/datasets](https://disc.gsfc.nasa.gov/datasets)). (Recently accessed date: 25 March 2022). We are grateful to the NASA Goddard Space Center for providing the AIRS TCWV, TAS and SST datasets for this study.