1) Normalized precipitation indices such as the SPDI are particularly useful for drought monitoring as they measure the precipitation relative to what is expected climatologically for a specific location and season. However, it is not adequate for addressing the temporal variability of hazards associated with extreme precipitation. It is not the same to have an SPDI >2 during the rainy season, which could imply pluvial and fluvial flooding and debris flows, and during the dry season, in which the precipitation is unlikely to represent a similar hazard. It is important that the authors include the analysis of indices specific to extreme precipitation events (e.g. http://etccdi.pacificclimate.org/list_27_indices.shtml)

Response. We do agree with this important observation from Referee 1. We have generated important previous experience regarding the applicability of the SPDI in both, extreme wet or dry precipitation conditions (eg. Thielen et al. 2020, Thielen et al. 2021, among other works which are cited in the original version of the manuscript). From this experience, we realized that SPDI could be an appropriate analysis tool since El Niño generates in this region, rather than several isolated events, a prolonged and continuous extremely humid (SPDI >2) pulse encompassing, firstly the wet season and, later in a lesser degree, the dry season. Precipitations generated from a mega-Niño event are expected to occur in these conditions. SPDI, as in SPI, is designed to analyze accumulative processes rather than the effects of isolated extreme precipitation events. As shown by Thielen et al. 2020 and Thielen et al. 2021, as well as the present study, the main hazards, and affectations from the occurrence of an extreme El Niño event, occur during this prolonged wet pulse, and while SPDI >2. It is projected, as a second stage of the present study, to analyze with the assistance of other indexes) such as the ones recommended by Referee 1, the precipitation dynamics on a daily bases during the prolonged wet pulses of the different mega-Niño events.

2) It is necessary that the authors include a validation of the CHIRPS satellite product using rain gauge data, at least for a few representative sites, particularly in arid regions where it might be less reliable, and for extreme events on a daily scale (e.g. https://doi.org/10.1016/j.pce.2022.103184).

Response. We completely agree with this important recommendation from Referee 1. Thus, as for section 2.2 Data, the entire text has been restated as follows in the new version of the manuscript:

“Precipitation data was obtained from the Climate Hazards Group Infrared Precipitation with Stations (CHIRPS V2.0, https://iridl.ldeo.columbia.edu/SOURCES/.UCSB/.CHIRPS/.v2p0/.monthly/.global/). CHIRPS V2.0 is a quasi-global gridded rainfall time series dataset, spanning 50°S-50°N, from 1981 to near-present, with 0.05° resolution satellite imagery with in situ station data, with great applications in monitoring precipitation extremes (Funk et al., 2015). Precipitation layers derived from interpolations of data from climate gauge networks have proven to have some limitations (Deblauwe et al., 2016). CHIRPS provides reliable precipitation observations with high accuracy and is particularly suitable for areas with few rainfall gauges (Paredes-Trejo et al., 2016; Beck et al., 2017), especially over montane (López-Bermeo et al. 2022) or arid regions (Paredes-Trejo et al. 2017; Ramoni-Perazzi et al. 2021) where extreme events may be rather common. According to Beck et al. (2017), in a global-scale evaluation of 23 precipitation datasets, CHIRPS V2.0 tends to perform the best in the hydrological modeling of tropical regions, specifically in Central and South America. As for Ecuador, Thielen et al. (2021a) successfully tested its applicability in the spatial/temporal analysis of hydroclimatological extreme events in one of the most important and extended basins of the Ecuadorian Pacific slope. For the present study, monthly data for the time series Jan-1981/Dec-2018 were obtained from 456 rasters. Monthly and annual mean, as well as some other basic precipitation parameters, were obtained through GIS applications.”

Now, these modifications in the text imply four new citations. Thus, they have been accordingly included in the REFERENCES section of the new version of the manuscript. These are as follows:

Deblauwe, V., Droissart, V., Bose, R., Sonké, B., Blach-Overgaard, A., Svenning, J.-C., Wieringa, J. J., Ramesh, B. R., Stévart, T., and Couvreur, T. L. P.: Remotely sensed temperature and precipitation data improve species distribution modelling in the tropics. Glob. Ecol. Biogeogr., 25, 443–454. https://doi.org/10.1111/geb.12426, 2016.

López-Bermeo, C., Montoya, R. D., Caro-Lopera, F. J., and Díaz-García, J. A.: Validation of the accuracy of the CHIRPS precipitation dataset at representing climate variability in a tropical mountainous region of South America, Phys. Chem. Earth, 127, 10.1016/j.pce.2022.103184, 2022.

Paredes-Trejo, F. J., Alves-Barbosa, H., and Lakshmi-Kumar, T. V.: Validating CHIRPS-based satellite precipitation estimates in Northeast Brazil, Journal of Arid Environments, 139, 26-40, 2017.

Ramoni-Perazzi, P., Passamani, M., Thielen, D. R., Padovani, C., and Arizapana, M. A.: BrazilClim: The overcoming of limitations of preexisting bioclimate data. Int. J. Clim., 42, doi: 10.1002/joc.7325, 2021.

Regarding extreme events on a daily scale, it is projected that for next necessary step in this research is to analyze the dynamics of daily precipitations. Several authors state that CHIRPS may be also considered a reliable satellite-based rainfall data source for many geographical regions (eg. Valdés-Pineda et al. 2016; Baez-Villanueva et al. 2018).

3) The study by Kiefer and Karamperidou (2019; https://doi.org/10.1029/2018PA003423) is very relevant and the authors should compare the results to theirs in the manuscript.

Response. We completely agree with the recommendation of Referee 1 about comparing our results to those from Kiefer and Karamperidou (2019). In this regard, in the Discussion section, references have been made regarding the similarities in the results between both studies specific to the effects of different ENSO flavors on precipitation dynamics, as well as to the altimetric response of precipitation anomalies to EP and COA events.

The citation Kiefer and Karamperidou (2019) has been properly added to the REFERENCES section as:

Kiefer, J., and Karamperidou, C.: High‐resolution modeling of ENSO‐induced precipitation in the tropical Andes: Implications for proxy interpretation. Paleoceanography and Paleoclimatology, 34, 217–236, doi:10.1029/2018PA003423, 2019.

1. Why you use SPDI index to monitor precipitation spatial-temporal dynamics? It seems that this index is more appropriate for drought monitoring instead of precipitation monitoring. In the introduction part and material part, there is no review about the applicability of SPDI for extreme precipitation monitoring.

Response. The SPDI is very similar to the SPI (Standardized Precipitation Index). Although it is originally used for analyzing drought evolution, as in for the SPI, its application is not limited to dry events. It is a monthly rainfall index that is based on the calculation of accumulated monthly rainfall anomalies. The index is simple enough to be able to be developed routinely and in real-time in wide spaces and for numerous observation stations, which makes it useful for implementation in the monitoring and forecasting of unusually dry and wet conditions at different time and space scales of analysis. We have generated important experience regarding the applicability of the SPDI in both, extreme wet or dry precipitation conditions (eg. Thielen et al. 2020 and Thielen et al. 2021, both works cited in the original version of the manuscript). In the new version of the manuscript, here provided, additional arguments about the use of the SPDI have been included in the Material & Methods section.

2. Following above comment, as shown in Figure 2. (Ia, Ib, IIa and IIb), even though most precipitation extremes occur in the first half of the second year of the event, the SPDI index still indicates very humid in the second half year especially in the 82/83 and 97/98 EI Niño events, under the situation that the precipitation is almost zero. Thus, I further suspect the applicability of SPDI for extreme precipitation monitoring in this study.

Response. As in SPI, the SPDI is a monthly rainfall index that is based on the calculation of accumulated monthly rainfall anomalies. At this time scale of analysis, SPDI tends to gravitate toward zero unless a distinctive wet or dry trend is taking place. SPDI as in SPI-12 is designed not to analyze the effects of isolated extreme precipitation events, but rather the accumulative effect of an anomalously wet pulse(s) to which a high concomitant SPDI value can be associated with flooding and other hazards in the event of any additional precipitation.

3.Labels in Figure 2 and Figures should be clearer.

Response. Labels and characters in Figures 1, 2, and 3 have been modified to be clearer. The resulting figures have been included in the new version of the manuscript, to be provided when requested.

4. Obviously SPDI index is a very important part in this study, but in the abstract, there is no introduction about SPDI. Please add the information of SPDI in the abstract.

Response. Although we do agree with Referee 2 about the strategic value of including additional and specific information about SPDI in the Abstract, this section has already reached the maximum allowed number of words. In any case, the present study is about the analysis of precipitation anomalies generated by extreme El Niño events, and not about the analysis tool itself. In the current form, in the Abstract, the results from such anomaly analysis are sufficiently addressed. As mentioned in our response to comment 1, in the Material and Methods section of the new version of the manuscript, additional information has been included regarding different medullar aspects of the SPDI, such as its applicability and performance regarding other important precipitation anomaly indexes such as the SPI.    

5.In session 2.4, why not re-sample SPDI estimation at 30 m resolution instead of resampling DEM at 0.05°, by which the altitudinal dynamics estimations would be more correct.

Response. The work resolution is determined by the lowest resolution available in any of the considered variables. Thus, any resampling must be performed toward the coarsest resolution. In our study, that of the precipitation anomaly data (ie. CHIRPS with 0.05° of spatial resolution).

6.Because Central Pacific El Niños, Eastern Pacific El Niño, and Coastal El Niño are mentioned many times in the paper, please make a figure to depict where are the regions of Central Pacific El Niños, Eastern Pacific El Niño, and Coastal El Niño.

Response: For an El Niño event to be categorized as CP, EP or COA it depends on the SSTA pattern. Along an ENSO event, the warming and presence of seawater may be temporal and spatial very dynamic. Such dynamics may vary a lot, not only among the different types of El Niño (CP, EP vs. COA), but also among El Niño events of the same type (eg. EP-EN82/83 vs. EP-EN97/98). These pattern or dynamics is a topic under a great deal of analysis and discussion. Regarding the objectives of the present study, it is out of our reach to elaborate a map (a sequence of maps, really) depicting the specific oceanic areas where each of the mega-Niño events, considered in the present study, expressed its SSTA dynamics. In any case, as stated in the original text of the manuscript (page 6, lines 18-19), readers can relate the CP-EN events occurring mainly in the central Pacific NIÑO 3.4 region, while the EP-EN and COA-EN occurring to the easterly Pacific region of NIÑO 1+2 region (Larkin and Harrison, 2005; Ashok et al., 2007; Kug et al., 2009).  

7.In the page 9. Line 17 to 18, it is confused that the sum of precipitation is only 17%, not 100%. Please clarify it.

Response. If March-July (n=5) has 10% each, and the rest of the months (n=7) is around 7% each, the sum is not 17%, but: (10% x 5 months) + (7% x 7 months) ≈ 100%. Now, to prevent any misinterpretation the text in the original manuscript (page 9, lines 17 to 18) has been rephrased as: “The monthly precipitation from March to July is about 10%, that is 50% of the annual total amount. As for the rest of the year, that is from August to February, precipitation discretely drops to around 7% per month.”