## 4 Discussions  
Water vapor is one of the main greenhouse gases and has an important contribution to the greenhouse effect, accounting for about 60-80% of it, which is significantly higher than other greenhouse gases. Theoretically, the increase in temperature and the water vapor content have a mutual feedback effect. Global warming will lead to an increase in water vapor content, and the increase of water vapor will promote the enhancement of the greenhouse effect, thereby leading to global warming. In this paper, the SST data of China from 2002 to 2022 provided by AIRS were used. Considering that the TCWV is highly related to SST, the Pearson correlation coefficients of TCWV with SST were calculated (Figure 12a). A strong correlation existed between TCWV and SST with a correlation value of 0.93. Many studies have shown that TCWV is highly influenced by SST [39; 40]. Figure 12b shows the spatial distribution of the average SST in China over the past 20 years. Its distribution is high in the east and low in the west, high in the south and low in the north, which has a high similarity with the spatial distribution of the TCWV. Thus, the higher the temperature, the higher the corresponding water vapor. The reason is mainly determined by the Clausis-Clayperon equation. The higher the temperature, the stronger the water storage capacity of the atmosphere [41]. As one of the most important components of the lower atmosphere, water vapor is a necessary condition for the formation and evolution of weather and climate phenomena such as precipitation and is also an important factor affecting the generation and development of extreme weather. Studying the temporal and spatial variation of water vapor is an important aspect of studying climate change, which is of great significance to further understand the working mechanism of the entire climate and weather system. In this paper,  
Figure 11: Prediction result of SARIMA model. Figure 10: Residual test. we studied and analyzed the spatial-temporal, inter-annual and seasonal distribution of TCWV in China based on AIRS data. We also used the SARIMA model to extract sufficient information from the time series system using the time factor as a comprehensive factor to analyze and predict the monthly average changes of TCWV in China. In China, studies have shown that the changes of water vapor not only have a greater correlation with the occurrence of droughs and floods in China, but the changes in water vapor affect the distribution of water resources in China. Therefore, an in-depth understanding of the temporal and spatial distribution of water vapor not only helps to study the impact mechanism of regional climate change under the background of global warming, but also provides a theoretical basis for the effective utilization of atmospheric water resources. The EOF analysis has been used by experts and scholars in recent years to further investigate water vapor. In this study, we conducted the EOF analysis on the annual average TCW from 2003 to 2021. It was found that the first variance contribution of the EOF analysis of TCWV is 31.47%, and the cumulative variance contribution of the first three modes is 59.73%, which means that the spatial distribution of the first three eigenvectors can represent the main distribution of water vapor content throughout the year. [PERSON] et al. [42] performed an EOF analysis on the precipitation in east China. The result showed the leading EOF pattern with an explained variance of 16%. There is also a clear dipole structure with the positive phase in the north and the negative phase in the south. The correlation coefficient between the series of leading principal component series and precipitation is 0.75, with a confidence level of exceeding 99%. Therefore, the first EOF is a good representation of precipitation variability in northern China.