## 2 Materials and Methods
### Methodology  
We analyzed five cases of extreme events to evaluate eleven satellite precipitation products. These cases occurred in different regions of Brazil and were selected in three stages. Firstly, with rain gauge data organized in grid points, we calculated a 99 th percentile threshold and used rain values above this number. These were considered extreme rain events. Second, for each proposed region, we estimated the mode of the dates when extreme rain occurred. Finally, having identified the most repeated date, we composed a five-day series (rainy pentad) by selecting two days forward and two days back (D-2, D, D+2) from the mode. This series represented the extreme event in each region. We used information about these extreme rain events and their impact from newspapers, photographs, and civil defense reports from the Integrated Disaster Information System--S2 ID to describe each event. Table 2 summarizes available information about each event. We analyzed the ratio between the amount of extreme rain the MCS influenced over the total rain recorded during extreme events to verify the influence of MCS on extreme rain on a sub-synoptic scale. Different meteorological systems can cause significant rain accumulations in Brazil. However, the MCS are usually responsible for the most extreme events; a few hours can provoke natural disasters that generate substantial social and economic losses. Therefore, we analyzed the MCS more thoroughly. Figure 2 shows a schematic flowchart with the necessary steps for the analysis. These steps are: (i) accumulated rainfall in five days from the rain gauge dataset, (ii) number of MCS events by grid point from \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Satellite Product Version** & **Short Name** & **Use Rain Gauges** & **Use IR Sensor** & **Use MW Sensor** \\ \hline CHIRP v2.0 & CHIRP & No & Yes & No \\ CHIRPS v2.0 & CHIRPSg & Yes & Yes & No \\ PERSIANN CDR v1 r1 & PERSIANNg & Yes & Yes & No \\
3B42 RT v7.0 unclablated & 3B42 & No & Yes & Yes \\
3B42 RT v7.0 & 3B42g & Yes & Yes & Yes \\ GSMAP-NRT-no gauges v6.0 & GSMAP & No & Yes & Yes \\ GSMAP-NRT-gauges v6.0 & GSMAPg & Yes & Yes & Yes \\ CMORPH V1.0, RAW & CMORPH & No & Yes & Yes \\ CMORPH V1.0, CRT & CMORPHg & Yes & Yes & Yes \\ TAPEER v1.5 & TAPEER & No & Yes & Yes \\ COSCH & COSCHg & Yes & Yes & Yes \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Description of satellite products, adapted from [44]. CACATOES dataset, (iii) percentage of days with MCS obtained by counting the days that recorded MCS events divided by five, and (iv) contribution of MCS in the total rainfall of the extreme event. To estimate the contribution of MCS, we first selected the rain pentads for each region from the INPE' rain gauge dataset in the one-degree spatial resolution and the daily temporal resolution(one degree by one day grid point). Second, we selected the CACATOES data within the same grid point and used the MCS events recorded (Daylymcs_pop). If at least one MCS event and a value of extreme rainfall co-occurred in a grid point, we aggregated the recorded rainfall (\(\sum\)\(Extreme\)\(Rain\)\({}_{MCS}\)). Third, we summed extreme rainfall in five days at each grid point (\(\sum\)\(Extreme\)\(Rain\)\({}_{total}\)). Finally, we estimated the ratio between both measures (Figure 2). The contribution of MCS to the total rainfall during an extreme rain pentad is expressed by the following equation:  
\[Contribution[\%/5\ days]=\frac{\sum\(Extreme\ Rain\)\({}_{MCS}\)\(\sum\)\(Extreme\ Rain\)\({}_{total}\)\(*\) 100  
where the \(Extreme\)\(Rain\)\({}_{MCS}\) represents the rain recorded at one daily degree for each grid point where the MCS was registered, and \(Extreme\)\(Rain\)\({}_{total}\) is the accumulated rain in five days at the grid point. We generated a contingency table based on the P99 th threshold of rainfall at each grid point. We used this table to compute the number of Hits (A), False Alarms (B), Misses (C), and Correct Negatives (D) for case studies considered in this work (Table 3).