## 4 Discussion  
Our observations show that the two stations TUCU and CATA in the eastern central Andes, had different rainfall distributions. This can be attributed to the fact that each station is located at different topographic and climatic regions. Past studies [4; 5; 11] investigated the different characteristics of rainfall along the climatic and topographic gradient over the south-central Andes. We show that the GNSS-IWV time series coincide with rainfall minima during the austral winter and maxima during the austral summer. Past studies also indicated a nearly homogeneous annual cyclical signal for GNSS-IWV data and a relation with heavy rainfall in Spain [13]. Our wavelet coherence analyses supports a seasonal agreement between GNSS-IWV and rainfall as well as CAPE and rainfall. Based on our results from the correlation between rainfall and GNSS-IWV, we show that the relation between both variables at the daily scale can be explained with an expo  
Figure 11: Averaged GNSS-integrated water vapour (blue line) and CAPE (red line) for the TUCU and CATA stations for the 90 th percentile rainfall (**A,D**), for the 95 th percentile rainfall (**B,E**), and for the 99 th percentile rainfall (**C**,F**). We selected all times with rainfall above the 90 th, 95 th, and 99 th percentiles, respectively and their corresponding GNSS-integrated water vapour and CAPE amounts. We then show the correlation for 72 h (event day plus day before and day after). Note that the GNSS-integrated water vapour and CAPE generally increase during the day before the event and that peak values—both for GNSS-integrated water vapour and CAPE—are observed at the day of the 90 th, 95 th, and 99 th event rainfall. nential relationship. In order to explain the extreme rainfall over the eastern central Andes, where convection plays an important role in extreme rainfall generation, it is important to consider multi-proxy approaches, including both dominant climatic variables (GNSS-IWV and CAPE) to separate out the effect of both variables in the presence of each other on rainfall. Previous research by [4; 26; 27] suggested a model that displays an exponential relationship between rainfall and the dew-point temperature and describes a power-law relationship between rainfall and CAPE. Based on the fitting function analysis in this study, we show that GNSS-IWV can be substituted as a reliable humidity data together with CAPE to analyse extreme rainfall. They are both responsible for convection and extreme rainfall. Since the rainfall data at both stations show a skewed distribution, quantile regression analysis can be considered as a suitable statistical analysis. Several previous research studies [51; 52; 53] also used quantile regression as a reliable statistical tool for analysing extreme rainfall events. Therefore, we investigated the joint effect of both climatic variables on extreme rainfall for the austral summer season relying on quantile regression [47]. We showed that the GNSS-IWV is more important for extreme rainfall at the TUCU station compared to the CATA station. This argument is supported by the lower contribution of GNSS-IWV to rainfall for CATA station at the 0.9 quantile compared to lower quantiles. This may be related to the fact that extreme convection has occurred more often in the northern (tropical) part of the Andes, where wide convective cores are part of a large mesoscale convective system and are more frequently observed than deep convective cores [46]. The correlation between rainfall and CAPE indicates that there is a higher contribution of CAPE at the CATA station compared to the TUCU station for extreme rainfall events. Previous research, [4] also indicated a higher importance of CAPE for extreme rainfall in the transition zone between the tropical and subtropical regions compared to tropical regions. These regions have been identified by the intense rising of warm and moist air that triggers the formation of deep convective storms [1; 4; 10; 46].