The need for better spatial and temporal resolutions of hydrological data has been an ongoing demand by researchers. To meet the demand, for example, with the advancement of technology and resources, the joint collaboration of NASA and USGS has decided to improve the spatial and temporal resolutions of its Landsat products (e.g., Landsat 8, 9) by 2030 with the impending introduction of Landsat NEXT, which is expected to have a spatial resolution of 10 m for some of the bands. In this manuscript, the authors propose a methodology to generate gridded rainfall data for a river basin in Australia. The spatial and temporal resolutions of the generated data are 1 h and 1 km, respectively. The proposed methodology is based on a thin-plate spline interpolation model that was introduced in 1990. The authors claim that the generated gridded rainfall data has outperformed the other sources of rainfall data available in Australia.

The current version of the manuscript has many flaws. I suggest the authors go through the video on how to write a research paper (https://www.egu.eu/webinars/229/how-to-write-a-research-paper/). Although the presentation is not the best on the topic, I believe that it has something to grasp. Thanks to the UK (University of Leeds), even after Brexit, it is playing the role of gladiator to safeguard the education system of the EU.

1) The title of the manuscript needs to be examined by the journal office. I presume that the journal office has appointed a handling editor and referees after going through their academic transcripts and their PhD works.

There is a need to understand the way titles have been coined in manuscripts in this journal office. For example, recently, in this journal office, the handling editor published a manuscript authored by Geogle Research. The title of the manuscript is Never Train a Long Short-Term Memory (LSTM) Network on a Single Basin. Is there a verb in the title? The title of this manuscript is "VERY HIGH spatial and temporal resolution rainfall data for accurate flood inundation modeling." Is there a verb in the title?

To evaluate the quality of the review process of the journal office, it would be more appropriate to conduct research to examine the titles of the manuscripts that have been published by the journal office since the establishment of the journal.

2) See the attached file (Figure 2_3.pdf). Are the figures correct? Have you pasted incorrectly? I am expecting the investors of the journal office to spend a few euros to develop an AI/ML-generated model to do a preliminary check on the manuscripts that are submitted to this journal office.

3) Refer to PART II

1-3) Refer to PART I

4) Australia is a developed country. If I have misunderstood, do correct me. Therefore, I am expecting the authors to produce their own datasets to map Figure 1. There are too many sources/references cited to produce the map shown in Figure 1. This gives a bad impression about the state of water resources management in Australia. Since the spatial locations of raingages are available from the Bureau of Meteorology, it should be a matter of a few minutes to have the map of gages. Moreover, Australia has a fine resolution DEM. Therefore, producing the catchments and stream networks for the nation is not a pain-taking task. Are the authors lacking knowledge and experience?

5) In the current version of the manuscript, the thresholds that are used to remove unrealistically high hourly and daily rainfall data are not justified. What was the reason to set the daily threshold to 1500 mm/d? What was the reason to set the hourly threshold to 300 mm/h? Considering the authors’ statements [LN 171-172, LN 106-107, LN 104] in the current version of the manuscript, these threshold values are not justified.

6) LN 170-Some unusually high values of hourly rainfall, mostly occurring at midnight, were detected. LN 168- The suspicious data were removed, including negative, ...

The authors have found some interesting points in the Australian datasets. It would be more appropriate to include a few lines to elaborate more on this for the readers to learn more on what has been found by the authors. What was the reason to have negative values in the dataset? What was the reason to have very high values of hourly rainfall, specifically at midnights? Are these observational errors, or are these due to some interesting unexplored researchable areas?

7) Refer to PART III

1-6) Refer to PART I & II

7) As per the authors, the spatial extent of the analysis is 30,389 km²[LN 111]. Moreover, there are 139 hourly stations for generating hourly rainfall surfaces using the proposed method. This includes the disaggregated stations (i.e., daily to hourly) as well. Considering these details, on average, a gauge accounts for 218 km²(= 30,389/139). Based on this information, you have fitted your 1 km gridded surface. Is this what you want to achieve in this manuscript? If I have misunderstood, do correct me.

8) Table 1 is a good piece of work in this manuscript. It would be more useful for the readers of this manuscript if the authors added a few more sections to outline more about the datasets (e.g., ANUClimate and AGCD) that the authors have listed in Table 1. Moreover, I suggest the authors work with the handling editor to find a better title for Table 1.

9) The spatial and temporal resolutions of ANUClimate are 1 km and 24 h, respectively. What if we disaggregate the daily grid of ANUClimate to an hourly grid using the existing hourly rain gauges? Wouldn't it yield the grid surface of your interest (i.e., 1 km in spatial and 1 h in temporal)? How would this differ from your methodology?

10) Refer to PART IV

1-9) Refer to PART I, II, and III

10) As per the authors, the radar rainfall shows the rainfall in the atmosphere instead of the rainfall reaching the ground. Moreover, as per the authors, the Bureau of Meteorology has found that there are errors in the radar-derived rainfall data, showing unreasonable high rainfall values in some areas. Due to this reason, the authors acknowledge that the hourly rainfall accumulating from radar data was not used in the analysis [LN 146-151].

With the advancement of technology and theoretical development, researchers have been employing ML and AI algorithms to learn from data. Considering this trend, wouldn't ML/AI algorithms improve the development of gridded rainfall surfaces of your interest (i.e., 1 km in spatial and 1 h in temporal)?  With the availability of daily rainfall gauges, hourly rainfall gauges, and hourly radar rainfall data, wouldn't it be feasible to develop an ML algorithm to relate the radar data and the existing hourly and daily gauged data to predict the correct values of hourly radar values (i.e., 1 km in spatial and 1 h in temporal)? How would this differ from your methodology?

11) Refer to PART V

1-10) Refer to PART I-IV

11) A few instances of ambiguous citing have been found in the current version of the manuscript. For example,

Statement #1-LN 18-21

High resolution temporal and spatial representations of precipitation data are required in many hydrological applications, such as modelling flood inundation (Jhong et al., 2017; Pappenberger et al., 2005), analysing catchment responses in rainfall-runoff models (Xu et al., 2022; Pappenberger et al., 2005; Acharya et al., 2019), and forecasting extreme events and natural hazards (Ficchi et al., 2016; Mukherjee et al., 2018).

Statement #2-LN 365-366

Hourly rainfall data are essential for many hydrological, ecological, and meteorological applications (Lewis et al., 2018; Hatono et al., 2022).

Why would Lewis et al. (2018) and Hatono et al. (2022) not fit into statement #1 in the current version of the manuscript? I suggest the authors work with the handling editor to resolve these issues.

12) As per the authors, the independent variable transformation for the DEM is x/a, where “x” is the DEM value, and “a” is the transformation parameter. In this study, “a” was set as 10,000 to reduce the impact of the DEM on the hourly rainfall surfaces. The usual value recommended for interpolating monthly and daily data is 1000 (LN 202-203).

The latitude and longitude values are in decimal degrees. The DEM is in liner unit (e.g., m). The rainfall data is in mm. Therefore, there exists a non-normality issue with the data that have been used to fit the surface. I am not sure if the authors are aware of this. Does this lead to setting a random number to the value of “a” (e.g., 9999 instead of 10000)? The current version of the manuscript doesn’t justify the magnitude of “a”. Why would it jump to 10,000 from 1000 just because the work is hourly instead of daily or monthly?

13) Refer to PART VI