This manuscript presents new magnetic-parameter and median grain-size records from the Gansen section in the western Qaidam Basin and integrates them with previously published records to reconstruct hydroclimate variations during the Late Pliocene to Early Pleistocene. The study addresses an important question: whether the precession-dominated precipitation pattern previously identified during the mPWP was persistent throughout the Late Pliocene or restricted to a specific warm interval.

The manuscript is clearly structured, the scientific question is relevant, and the new data provide useful information for understanding orbital-scale hydroclimate variability in the Asian interior. The comparison between the 3.6–3.3 Ma interval and the previously published 3.25–2.95 Ma interval is particularly interesting. The finding that precipitation variability may have shifted from a 100-kyr-dominated pattern to a precession-dominated pattern under different background climate states is potentially significant.

Overall, I find the manuscript suitable for publication after minor revision. The main conclusions are generally reasonable, but several points require clarification, especially regarding the age model, spectral-analysis procedures, proxy interpretation, and the wording of the forcing mechanisms.

Reply: Thank you for the encouraging comments.

1. The chronology is based on the previously published paleomagnetic framework. Because the manuscript focuses on orbital-scale variability, the age model should be described in slightly more detail. I suggest that the authors add a short summary of the main magnetostratigraphic tie points, interpolation approach, and sedimentation-rate changes. It would also be helpful to briefly mention how chronological uncertainty may affect the identification of 20-kyr, 40-kyr, and 100-kyr cycles. This does not require a new age model, but the current manuscript would benefit from a clearer statement of the chronological basis of the orbital interpretation.

Reply: Thanks for the suggestion and we have expanded the description of the GS age model in the materials and methods section (Lines 159-177).

Lines 159-177: 3.1 Chronology of the GS section

The chronology of the GS section is based on the previously published paleomagnetic framework of Luo et al. (2024). In that study, the observed polarity sequence of the sampled 0-453 m interval was well correlated with the geomagnetic polarity time scale (GPTS), and 10 paleomagnetic age control points were used to establish the age model for the section, indicating that the sampled interval spans ~3.6-~1.8 Ma (Luo et al., 2024). Ages for depths between adjacent paleomagnetic tie points were obtained by linear interpolation, and the resulting age-depth relationship indicates that sedimentation rates were not strictly uniform throughout the GS section. Because the age model is constrained by discrete paleomagnetic tie points, some uncertainty remains in the interpolated ages between adjacent control points, potentially affecting the identification of orbital cycles. Nevertheless, the paleomagnetic chronology is broadly consistent with the Plio-Quaternary age assignment of the GS strata on the regional geological map (Luo et al., 2024), providing an independent geological framework for the studied interval. Furthermore, the same paleomagnetic framework has successfully resolved significant orbital cycles in the previously published high-resolution GS record for 3.25-2.95 Ma (Luo et al., 2024), supporting the use of this chronology for orbital-scale analysis in the studied interval.

1. The spectral results are important for the main conclusion, but the methods are not described in sufficient detail. Please specify the spectral-analysis method used, the interpolation step, detrending method, red-noise background, confidence-level calculation, and filtering procedure. The figure captions should also include enough information for readers to understand how the spectral peaks and filtered curves were obtained. The authors may also consider adding a short statement on the robustness of the 100-kyr peak during ~3.6–3.3 Ma, given that this interval contains only a limited number of eccentricity-scale cycles.

Reply: Thanks for the suggestion. We added introduction of spectral-analysis method (Lines 198-215). We also revised the figure 4 caption to include the key methodological information needed to interpret the spectral peaks and filtered curves (Lines 245-250). In addition, we added a short statement in the discussion section (Lines 382-387) to clarify the robustness of the 100-kyr peak during 3.6-3.3 Ma.

Lines 198-215: 3.3 Spectral analysis

The average sampling resolution of the GS section is ~4 kyr for the ~3.6-3.3 Ma interval and ~19 kyr for the 2.95-~1.8 Ma interval, indicating that the ~3.6-3.3 Ma record has sufficient temporal resolution to resolve orbital-scale variability. For spectral analysis of the ~3.6-3.3 Ma interval, the χ_fd/HIRM and median grain-size records were first converted from depth to time based on the paleomagnetic age model of the GS section and then interpolated to evenly spaced 1-kyr time series. Power spectra were calculated using the REDFIT procedure implemented in PAST 3 software, which estimates the red-noise background and tests spectral peaks against an AR(1) red-noise null model. Spectral peaks exceeding the 90% red-noise confidence level were regarded as statistically significant. No additional detrending was applied prior to the Redfit analysis.

Band-pass filtering was further applied to extract the 20-kyr, 40-kyr, and 100-kyr components. The central frequencies and bandwidths used for filtering were 0.05 kyr⁻¹ and 0.012 kyr⁻¹ for the 20-kyr band, 0.024 kyr⁻¹ and 0.004 kyr⁻¹ for the 40-kyr band, and 0.01 kyr⁻¹ and 0.002 kyr⁻¹ for the 100-kyr band, respectively. The filtered GS records were then compared with the corresponding filtered insolation, eccentricity, and benthic δ¹⁸O stack.

Lines 382-387: Although the ~3.6-3.3 Ma interval spans only ~0.3 Myr and therefore contains only a limited number of 100-kyr cycles (Fig. 5), both χ_fd/HIRM and median grain-size records consistently exhibit a prominent 100-kyr signal (Fig. 4). This interpretation is further supported by the similar long-term variations in the two precipitation records and by the similar patterns of their filtered 100-kyr components (Fig. 3 and 6).

1. The interpretation that the 100-kyr cyclicity reflects a nonlinear response to insolation forcing is interesting and reasonable. However, eccentricity itself has a weak direct effect on insolation, so the mechanism should be described more cautiously. I suggest using wording such as “may reflect” or “is consistent with” rather than stronger statements implying a direct causal relationship. The possible roles of Antarctic ice-sheet variability and annual insolation are worth discussing, but they should be presented as plausible hypotheses rather than fully demonstrated mechanisms

Reply: Thank you for this helpful comment. We agree that the proposed mechanism should be described more cautiously. Accordingly, we have revised the relevant text throughout the manuscript by replacing stronger causal wording with more cautious expressions such as “may reflect” We have also revised the discussions of Antarctic ice-sheet variability and annual insolation as possible explanations rather than demonstrated mechanisms, emphasizing that they represent plausible hypotheses requiring further investigation (Lines 415-433).

Lines 415-433: One possible explanation is that eccentricity-scale forcing may have modulated high-latitude ice-volume variability, including Antarctic ice-sheet variability, in a nonlinear way, and that these changes may in turn have influenced EASM intensity through changes in the thermal contrast between Eurasia and the surrounding oceans and/or through changes in Pacific and Atlantic meridional overturning circulation (Zhang et al., 2022; Wang et al., 2023). Because the Arctic ice sheets did not develop on a large scale before ∼2.7 Ma (Brigham-Grette et al., 2013; Rahaman et al., 2020; Bridges et al., 2023), Antarctic ice-sheet variability was likely an important component of high-latitude ice-volume changes during the Late Pliocene. Thus, at the 100-kyr band, the broadly similar variations between the two GS precipitation records, eccentricity, and the benthic δ18O stack are consistent with the hypothesis that eccentricity-paced high-latitude ice-volume variability, potentially involving Antarctic ice-sheet changes, contributed to the observed 100-kyr signal in the GS section (Fig. 8). Another possibility is that the dominant 100-kyr cycles in the precipitation records could have been controlled by annual insolation instead of summer insolation. Because the annual insolation integrates seasonal insolation variations caused by precession, it has more power in eccentricity bands and weak power in precession bands (Laskar et al., 2004). However, more investigations are needed to validate this hypothesis.

1. The use of χ_fd/HIRM as a precipitation-sensitive proxy is appropriate and is supported by previous work in the Qaidam Basin. However, it would be helpful to briefly summarize why this ratio can be used in the GS section. In particular, the authors should clarify whether post-depositional dissolution, evaporite dilution, or changes in sediment source are unlikely to dominate the magnetic signal.

Reply: Thanks for your careful review. We have revised the manuscript accordingly. In the revised text, we further clarified the applicability of χ_fd/HIRM as a precipitation-sensitive proxy in the GS section by adding a brief discussion of potential non-climatic influences. In particular, we now explain why post-depositional dissolution, evaporite dilution, and sediment-source changes are unlikely to have dominated the observed χ_fd/HIRM variations in the studied intervals (Lines 304-322).

Lines 304-322: However, to interpret χ_fd/HIRM as a precipitation proxy in the GS section, potential non-climatic influences, including post-depositional dissolution, evaporite dilution, and changes in sediment source, need to be considered. First, thermal demagnetization results reported by Luo et al. (2024) for the GS section indicate the preservation of magnetite-bearing remanence carriers, suggesting that pervasive post-depositional reductive dissolution was unlikely to have dominated the magnetic signal. Previous studies from Qaidam fluvial-lacustrine sediments further support the applicability of χ_fd/HIRM as a precipitation proxy in settings where magnetic minerals were not significantly dissolved after deposition (Nie et al., 2017; Su et al., 2019; Ren et al., 2020). Second, although evaporite-bearing lithologies occur in the section, the χ_fd/HIRM variations are difficult to explain by simple evaporite dilution alone, given their broad consistency with the median grain-size record (Fig. 5). Third, the GS section is dominated mainly by fine-grained lacustrine deposits with relatively uniform lithology, suggesting that major changes in sediment provenance were unlikely to be the primary driver of the observed χ_fd/HIRM variations. These considerations support the interpretation that χ_fd/HIRM in the GS section primarily reflects precipitation variations rather than being dominated by post-depositional dissolution, evaporite dilution, or sediment-source changes.

1. The interpretation of coarser median grain-size as reflecting stronger precipitation is plausible in a closed shallow-lake setting. However, hydrodynamic conditions should be considered. The grain size of fluvial, delta or nearshore lacustrine sediment is correlated with hydrodynamic conditions, which, in turn, are closely linked to precipitation variability. Therefore, it can indirectly indicate changes in precipitation patterns.

Reply: We agree with the reviewer that hydrodynamic conditions should be considered. Accordingly, the relevant text in the revised manuscript (Lines 322-328).

Lines 322-328: The median grain size can also serve as an indirect precipitation proxy because the grain size of fluvial, deltaic or nearshore lacustrine sediments is closely related to hydrodynamic conditions, which, in turn, are strongly influenced by runoff and precipitation. In the Qaidam Basin, coarser particles in closed and shallow lacustrine settings generally reflect stronger hydrodynamic conditions associated with increased runoff and precipitation (Su et al., 2019; Yang et al., 2020; Luo et al., 2024).

6. The phase relationship between the GS records and the benthic oxygen isotope stack at the 40-kyr band is interesting. However, the benthic δ18O stack reflects both global ice volume and deep-water temperature, and it should not be treated as a pure Antarctic ice-sheet record. I suggest revising the relevant sentences to “high-latitude ice-volume and/or deep-ocean temperature changes” or “Antarctic ice-sheet variability may have contributed to the observed 40-kyr signal.” This would make the interpretation more cautious and scientifically precise.

Reply: We agree that the benthic δ¹⁸O stack reflects both global ice volume and deep-water temperature and therefore should not be treated as a pure Antarctic ice-sheet record. Following this suggestion, we have revised the relevant sentences to make the interpretation of the 40-kyr signal more cautious and precise. In the revised manuscript, we now state more cautiously that high-latitude ice-volume variability, potentially including Antarctic ice-sheet variability, may also have modulated precipitation variations in the basin.

1. The title says “Late Pliocene,” but the record extends to 1.8 Ma. Please consider using “Late Pliocene to Early Pleistocene” where appropriate.

Reply: We appreciate the reviewer's suggestion. Although the GS record extends to ~1.8 Ma, the orbital-scale analyses and the main conclusions of this study focus primarily on the Late Pliocene, because the sampling resolution in the Early Pleistocene interval is insufficient for robust orbital-scale analysis. We have therefore retained “Late Pliocene” in the title to reflect the primary focus of the study. At the same time, we have revised the title by replacing “hydroclimatic variations” with “precipitation variations” to better match the proxy interpretation and the main objective of the manuscript.

1. Please distinguish clearly between the formal mPWP interval, 3.264–3.025 Ma, and the broader 3.25–2.95 Ma interval used in the analysis.

Reply: We agree that the distinction between the formal mPWP interval (3.264-3.025 Ma) and the broader 3.25-2.95 Ma interval should be stated more clearly. To avoid possible ambiguity, we have checked the manuscript throughout and revised the relevant text where necessary to make this distinction clearer.

1. Line 38, “Precipitation variations during 3.6–3.3 Ma was characterized” should be revised to “precipitation variations during 3.6–3.3 Ma were characterized.”

Reply: We have revised this sentence carefully.

1. Lines 135-136 “major moisture source” should be “major moisture sources.”

Reply: Thank you for pointing this out. We have revised “major moisture source” to “major moisture sources”.

1. Line 211, “high-frequent fluctuations” should be changed to “high-frequency fluctuations.”

Reply: We have revised “high-frequent fluctuations” to “high-frequency fluctuations”.

12. Lines 382-383, “seasonable insolation variations” should be “seasonal insolation variations.”

Reply: We have revised “seasonable insolation variations” to “seasonal insolation variations”.

1. Line 384 “Lasker et al.” should be corrected to “Laskar et al.”

Reply: We have revised “Lasker et al.” to “Laskar et al.”.

1. The sample numbers should be clarified. The manuscript states that 149 samples were measured, but the numbers given for different intervals should be made consistent.

Reply: We agree that the sample numbers should be clarified. The total number of samples used in this study is 149, whereas the sentence in question was intended only to describe the average sampling resolutions of the two main analyzed intervals. To avoid confusion, we have revised the relevant text by removing the interval-specific sample numbers from that sentence and clarifying the total number of samples in the manuscript.

1. Figure 4 should include more methodological information in the caption, including the spectral method and confidence level.

Reply: We have revised the caption of Figure 4 accordingly by adding the spectral-analysis method and the confidence level used in the spectral analysis (Lines 245-250).

Author responses to Anonymous Referee #2:

I have read the manuscript together with the previous referee comments and the authors’ responses. The revision has addressed most of the issues raised in the first round, and the manuscript is now considerably clearer. I find the study suitable for publication after minor revision. My remaining comments mainly concern the strength of the orbital-scale interpretation.

Reply: We thank the reviewer for the careful evaluation of our revised manuscript and for the constructive suggestions.

1. I think a clearer distinction is still needed between sampling resolution and chronological resolution. The ~4 kyr value describes the average spacing of the samples in the age domain, whereas the uncertainty between paleomagnetic tie points may be appreciably larger. This matters particularly for interpretation of the 20- and 40-kyr components. I suggest providing the paleomagnetic tie points and the corresponding age-depth relationship, at least as supplementary material, together with a brief indication of sedimentation-rate changes and their possible effect on the shorter orbital bands.

Reply: We agree with the reviewer that sampling resolution and chronological resolution should be clearly distinguished. In the revised manuscript, we have clarified that the ~4 kyr value describes the average temporal spacing between samples, rather than the chronological resolution or precision of the paleomagnetic age model. Following the reviewer’ s suggestion, we have added the paleomagnetic tie points and the corresponding age-depth relationship as Supplementary Table S1 and Supplementary Figure S1a. The sedimentation rates between adjacent paleomagnetic tie points are listed in Supplementary Table S1 and illustrated in Supplementary Fig. S1b. We have also added a statement noting that, because ages between adjacent tie points were obtained by linear interpolation, chronological uncertainty between tie points may exceed the average sample spacing and may have a greater influence on the interpretation of the shorter 20- and 40-kyr components than on eccentricity-scale variability.

Lines 199-207: The average temporal spacing between samples is ~4 kyr for the ~3.6-3.3 Ma interval and ~18 kyr for the 2.95-~1.8 Ma interval. These values describe the spacing of the analyzed samples, rather than the chronological resolution or precision of the paleomagnetic age model. Because ages between adjacent paleomagnetic tie points were obtained by linear interpolation, chronological uncertainty between tie points may exceed the average sample spacing and may have a greater influence on the interpretation of the shorter 20- and 40-kyr components than on eccentricity-scale variability. Therefore, the spectral analysis was focused mainly on the densely sampled ~3.6-3.3 Ma interval.

1. About describing the ~3.6–3.3 Ma interval as showing a well-defined 100-kyr cycle. The interval is only about 300 kyr long and therefore contains approximately three eccentricity-scale cycles. In addition, the strongest peaks in the two proxies are not identical, with χfd/HIRM close to 100 kyr and median grain size closer to 125 kyr. Given the limited record length, it may be more appropriate to refer to “eccentricity-scale (~100–125 kyr) variability” unless the authors can demonstrate that these frequencies can be reliably distinguished.

Reply: We agree with the reviewer’ s suggestion. We have revised the relevant text by replacing “100-kyr cycles” with “eccentricity-scale (~100–125-kyr) variability” where appropriate.

1. The records were interpolated from an average sampling resolution of ~4 kyr to a 1-kyr time step before REDFIT analysis. The rationale for this interpolation should be stated. Since interpolation does not add independent temporal information, it would also be useful to confirm that the main eccentricity-scale spectral result is not sensitive to this processing step.

Reply: We agree that the rationale for interpolation should be stated more clearly. In the revised manuscript, we explain that the 1-kyr interpolation was used only to obtain evenly spaced time series for spectral analysis and band-pass filtering, and that this procedure does not add independent temporal information to the records. To evaluate whether the main spectral result was sensitive to this processing step, we repeated the spectral analysis using a 2-kyr interpolation time step. The results show that the main eccentricity-scale (~100–125-kyr) variability remains evident in both proxy records, indicating that this feature is not an artifact of the 1-kyr interpolation. We have added this clarification to the Methods section and provided the sensitivity test as Supplementary Fig. S2.

Lines 210-212: The 1-kyr interpolation was used only to regularize the time spacing of the records for spectral analysis and band-pass filtering, and does not add independent temporal information.

Lines 216-221: To test whether the main eccentricity-scale spectral result was sensitive to the interpolation procedure, we repeated the REDFIT analysis using a 2-kyr interpolation time step. The eccentricity-scale (~100-125-kyr) variability remained evident in both proxy records, indicating that this feature is not an artifact of the 1-kyr interpolation (Supplementary Fig. S2).

Lines 269-272: A sensitivity test based on a 2-kyr interpolation time step yielded similar eccentricity-scale spectral peaks in both proxy records, further supporting that the main eccentricity-scale variability is not caused by the 1-kyr interpolation procedure (Supplementary Fig. S2).

1. The manuscript continues to use the term “phase relationship” when comparing band-pass-filtered records. It is not clear whether phase and coherence were actually quantified. If the comparison is based primarily on the visual alignment of filtered curves, wording such as “broad correspondence” or “similar filtered variations” would be more appropriate. This is especially relevant at the 20- and 40-kyr bands, where modest age uncertainty could affect the apparent alignment between records.

Reply: Thanks for the suggestion and we have replaced “phase relationship” with more cautious expressions such as “broad correspondence” and “similar filtered variations” where appropriate. We have also softened the interpretation of the 20- and 40-kyr bands in the revised manuscript.

1. The revised discussion of χ_fd/HIRM is more convincing, but would slightly soften the final statement. Preservation of magnetite-bearing remanence does not necessarily rule out selective alteration of the fine magnetic fraction, and relatively uniform lithology does not completely exclude smaller provenance changes. These considerations do not invalidate the use of χfd/HIRM as a precipitation-sensitive proxy, but minor sedimentological or post-depositional influences cannot be entirely excluded.

Reply: Thank you for this helpful comment. We have softened the final statement on the χ_fd/HIRM proxy interpretation. The revised text now states that χ_fd/HIRM primarily reflects precipitation variations in the GS section, while acknowledging that minor sedimentological or post-depositional influences cannot be entirely excluded.

Lines 336-340: These considerations support the use of χ_fd/HIRM as a precipitation-sensitive proxy in the GS section, although minor sedimentological or post-depositional influences, including selective alteration of fine magnetic particles, evaporite dilution, and small provenance changes, cannot be entirely excluded.