Thank you for taking the time to review this manuscript.

The point raised about possible correlations to the dominant climate modes affecting rainfall in Australia and other oceanic-state variables is an excellent one and we have considered it at great length. Initial results were not included in this paper for several reasons but mostly due to the scope and report length:

• Initial aim and scope: the purpose of this study was to test the previous findings that the 18.6-yr Lunar Nodal Cycle and ~11-yr sunspot cycles were drivers of Australian rainfall using more comprehensive data and rigorous statistical analysis. The unexpected finding was that although the lunisolar cycles did not appear to be present there were other clear cycles of slightly different periodicity.
• Paper length: having discovered the cycles, more emphasis was put on a complete description and exhaustive statistical testing. Much of this work needed to be edited down to fit in a single paper and adding in wider climate variables would have involved either sacrificing some of the evidence required to first substantiate the claim, or increasing the length of the manuscript (which is already quite long). Neither of these seemed like viable options.

It is likely that these cycles are ultimately related to, or mediated by, known climate drivers in some way. Though the influence of the El Niño-Southern Oscillation (ENSO), the Indian Ocean Dipole (IOD) and the Southern Annular Mode (SAM) are significant in Eastern Australia, their impact is not limited to this region alone. We decided that the next step should be to first look at global datasets for other regions which may show similar evidence of influence by these cycles and then consider how this could relate to broader climate variables. Work on this front is currently in progress, and the manuscript will be updated to reflect this rationale. Thank you for the direction to the Amonkar et al (2023) paper, it will be a valuable reference in the consideration of the relationship to climate indices and will be referenced in the revised manuscript as well.

Thank you for your detailed and insightful comments on the manuscript.

Regarding the first comment about the use of wavelet analysis, this method is indeed very common in the exploration of periodicity in hydrological data, such as rainfall (Chowdhury & Beecham, 2012; Murumkar & Arya, 2014; Santos & Morais, 2013; Williams et al., 2021) and streamflow (Briciu, 2014; Brown et al., 2021; Gorodetskaya et al., 2024). The paper will be updated to make note of this.  Its main advantage over something like a Fourier transform is the ability to identify (and easily visualise) modulation in the period and amplitude of cycles - therefore accommodating some of the non-stationary properties observed by previous researchers (Vines et al, 2004 - see Page 5 line 92). It was also the visualisation of cycle power in the time domain which allowed us to identify that was previously thought to be a "phase-change" was actually a change in the dominant cycle due to amplitude modulation. These advantages of wavelet analysis will be expanded upon in the methods section.

What was novel about the way wavelet analysis was used in this paper was the automated peak extraction and cluster analysis by Gaussian Mixture Model. This was a newly developed method which allowed for the analysis of a much large number of spectra than usual, along with testing statistical significance of the number of sites compared to red and white noise. The results of wavelet analysis are often interpreted visually and independently in a limited region, such as Chowdhury & Beecham (2012)- 53 rain gauge stations, limited to South Australia, Murumkar& Arya (2014) - 4 rain gauge stations, limited to the Nira Catchment in India, or Amonkar et al., (2023) - 24 sites limited to the Ohio River Basin in America. This method allowed for the combined analysis of 347 rain gauge stations covering an area of nearly 4 million km2 - this is the largest area covered by any study that we are aware of into periodicity in rainfall using wavelet analysis. We will modify the discussion to highlight this advantage.

Thank you for the feedback on the figures. It is good to know that most were successful at conveying the message.

Figure 1 - Agreed, we will update the figure and attempt to improve the clarity and resolution.

Figure 4 - Thank you for point this out, the colormap is matched to the cycles throughout the paper but changing the land colour to match the grey in Figure 9 should significantly improve the contrast and clarity of the cycle families. We will do this for all figures which feature blue landmass for consistency.

Figure 5 - The figure text will be updated to match the panels

Figure 6 - Formatting will be modified to increase visibility of the significant sites over red noise

Page 5 line 102: possible typo “p”lane. - corrected

Page 15 line 311: typo "20.4-yrcyclecan". - corrected

Page 22 line 427: possible typo “the allowed”.- corrected