## 3 Methodology and Study Area
All rain gauge data in Mexico was collected through the online database provided by the Mexico National Meteorological Service website ([[https://smn.conagua.gob.mx/es/](https://smn.conagua.gob.mx/es/)]([https://smn.conagua.gob.mx/es/](https://smn.conagua.gob.mx/es/))) accessed on 18 July 2019, which supply meteorological information and provides a quality-controlled dataset. The dataset consists of 2829 tipping bucket rain gauges, each with a temporal resolution of 24 h (UTC-6). From Figure 4 it can be observed that the distribution of rain gauges is uneven, which shows that the stations are accumulated densely in the south and spatially in the north. The reason for this is that the arrangement of rain gauges is based on the levels of precipitation density, so that there are more rain gauge stations in dense precipitation areas. This therefore reflects that southern Mexico receives more rainfall than the north. However, due to the frequency of measurement errors, traditional ground-based rain gauge measurements are not often regarded as a representative over large and high precipitation intensity areas, particularly at fine temporal resolutions. By contrast, satellite-based precipitation measurement has the potential to improve the understanding and forecasting of hydrological systems. In this study, the GPM-IMERG V5 near-real-time (NRT) run product at 0.1\({}^{\circ}\) spatial resolution (around 10 km but varies from the equator to the poles) and 30 min temporal resolution were used. The data were available in an RT-H5 file format (RT indicates real-time and H5 represents an HDF5 file, respectively) and downloaded from the NASA Precipitation Measurement Program website ([[http://www.pmm.nasa.gov](http://www.pmm.nasa.gov)]([http://www.pmm.nasa.gov](http://www.pmm.nasa.gov))) accessed on 24 July 2019. In addition, the dataset was translated to the daily time period as to correspond with the rain gauge data set as only daily gauge precipitation data were available. Therefore, a daily period between 8 am to 8 pm local time (UTC-6) was created within the dataset by the MATLAB package. The data period collection of rain gauges and GPM IMERG is shown in Table 2 below. Figure 3: The monthly average rainfall and temperature in Mexico (1943–2018) (source: National Water Commission (CONAGUA)). From the table, it can be seen that the same period of data collection of rain gauges and GPM is from 1 June 2014 to 1 March 2018 and the full-year data for both of them are from 2015 to 2017. Therefore, the precipitation data from this period of three years (2015-2017) was used in this analysis. Since the collected data is unstructured from the website, the processing the raw data into structured data takes considerable time for post processing. The distribution of mean annual precipitation in Mexico from 2010 to 2015 according to the National Water Commission (Figure 2), which was estimated by weather radar. In order to give a broad understanding of the quality of the GPM product for measuring precipitation over Mexico, Figure 5 was plotted by MATLAB to present the distribution of the mean annual precipitation estimated using GPM data over a three-year period from 2015 to 2017. By comparing these two graphs, it can be observed that the precipitation distribution estimated by GPM is quite close to the radar precipitation estimation, and the GPM is proven to be able to duplicate the spatial precipitation patterns while showing only slight overestimates in coastal areas. The preliminary analysis indicates that the quality of the GPM data performs well at the national scale. After evaluating the quality of GPM data at the annual and national scales, it was found that the GPM presented a relatively high agreement with rain gauges. It needs to be noted that the rain gauge data with zero was ignored in this study. The next stage of the study investigated the quality of GPM data at the daily scale by using single and multiple gauges.