## 4 Discussion  
From results found in this research, it can be said that the IMERG generally captures the trends of the average precipitation observed for all time scales analyzed (hourly, daily and seasonal). However, for the daily precipitation cycle, a 2-h lag in terms of the peak time was observed. [PERSON] et al. (2015) found a similar result but with a more severe lag between 2 and 5 h for six sub-regions of China. These lags have been associated to the use of geo-infrared satellites in the estimation of the precipitation ([PERSON] et al., 2015). rain gauges, but their analysis only included cases with observed precipitation. [PERSON] et al. [4] found overall positive biases over the Tibetan Plateau region using a three-hourly time scale. In addition, it was also observed that the probability for the IMERG to overestimate gauge data decreases with the meteorological station altitude for the hourly and daily precipitation data. [PERSON] and [PERSON] [20] found that the TRMM precipitation product overestimated rainfall on elevation ranges less than 3000 m but severely underestimated precipitation over higher elevations in the Himalayan region. The relation existing between all satellite precipitation products and the orography is undeniable [4; 20; 26]. However, in this study this relationship becomes more evident for the daily than for the hourly precipitation. The differences in biases between regions might be due to the randomly selected number of gauges used for bias correction in satellite products and/or due to the spatial resolution of the satellite-based precipitation estimates [3]. Another source for error in precipitation biases among regions is the types of typical clouds that differ from one area to the other. It has been said [27] that the precipitation produced by shallow orographic systems can be underestimated by microwave radiometer algorithms. Also, the presence of dense, cold cirrus clouds often leads to overestimation of rainfall [28]. Therefore, the variability and complexity of the Mexican orography is enough for the IMERG precipitation product to be conditioned by all error sources previously mentioned. On both shores of the country, there are low elevation stations with moist advection coming from the oceans. Inwards of the country, close to the coasts, the presence of high mountainous systems and high plains in the center of the country can be observed in Figure 1b. Therefore, the humidity is forced to rise due to orography, and deep cumulonimbus clouds are formed mainly in summer time, windward of the mountain systems spreading large areas of dense cirrus. This regional situation might easily lead to overestimation of precipitation. Also, one source of precipitation over the high plains in Mexico is from the shallow cumulus clouds that most often form during the dry season. However, during summertime, large convective systems may form during night-time hours leading to the overestimation of precipitation by IMERG. On the other hand, the analysis of the extreme precipitation events showed a different behavior of the IMERG precipitation estimates. Both hourly and daily cumulative precipitations from IMERG underestimate rain gauge extreme events by more than 60 and 40% of the mean rain gauges respectively. It may seem contradictory that the probability for IMERG to overestimate the hourly precipitation (see Figure 2) increases during night-time hours when there is more probability for precipitation to occur. However, this result is representative of the mean values of the hourly precipitation for which the mode of the precipitation (0 mm) plays an important role. It could also be related with the bilinear interpolation method applied to the IMERG gridded data in order to obtain point values comparable to rain gauges. The extreme events analysis in this study took into consideration the 98 th to 99.99 th percentiles of the hourly and daily precipitation distributions.