We thank you for your comments.

Here are point wise reply:

1. Lack of Clarity in Methods and Logical Exposition The manuscript suffers from poor readability and methodological rigor. Critical details are missing, for example:

Figure 2 presents correlation coefficients between signal and noise components, but it does not specify how these components were extracted from the raw data or model output;

Reply: In equation 3, which is based on Rowell et al. (1995), considers that total variance is the sum  of external (i.e. signal) and internal variance (i.e. noise). Lines 104-106 clearly mention, what are signal, noise and their ratio, i.e. signal-to-noise ratio (SNR).  Equations 1-3 and related description in lines 106-118 describe, how it can be calculated from  model output variable (assumed ‘x’ in equations) .  As per equation 3 or 4 signal and noise components are considered to be orthogonal.

In result section, line 197-1998 “This comprehensive analysis is conducted on seasonal (June-July-August-September) averaged rainfall, SST, MSLP, and 2-metre air temperature (land region) data of 1981-2021 …. “. So, here  rainfall, SST, MSLP etc are ‘x’ variable in equations 1,2.  This is a well known method and majority of predictability estimates are based on this method. We have cited several papers/books for further description (e.g.  Rowell et al. (1995); Rowell, 1998;  Kang and Shukla, 2006; Westra and Sharma, 2010). 

Figure 3 abruptly introduces the Niño3.4 index and regional precipitation without clarifying the research objective or the definition of "correlation skill" (e.g., does it refer to the correlation between the ensemble mean forecast and observations?). The role of Niño3.4 (as a predictor or a reference field) remains unexplained.

Reply: ENSO is the dominant mode of global climate variability and the primary large-scale predictor of both the Indian summer monsoon and tropical Pacific variability. Therefore, the Niño3.4 index was included to examine, by default, how the predictability of the predictor (Niño3.4) and the predictand (regional precipitation) compares with the actual skill achieved by the model. In other words, it provides a baseline for assessing the extent to which monsoon predictability arises from ENSO. Since ENSO remains the single most influential predictor of Indian summer monsoon rainfall, its inclusion is both natural and necessary. Therefore, we chose not to elaborate much, why we are using Niño3.4. Having said these, we are open to add in the manuscript briefly reason for using Niño3.4

Such breaks in logical flow and ambiguous methodological descriptions make it impossible for readers to understand the study process or verify the conclusions, severely violating standards of scientific writing.

We thank you for your comments, certainly we can add brief description to make it clearer to the readers.

2. Conceptual Confusion and Misuse of Terminology The manuscript contains inaccuracies in fundamental scientific expression:

It conflates the concepts of "Analysis of Variance (ANOVA)" and "Signal-to-Noise Ratio." ANOVA is fundamentally a statistical significance testing method, while the manuscript merely borrows its variance decomposition framework to estimate the signal-to-noise ratio without clarifying the distinction, which may mislead readers;

Reply: We do not fully agree with this statement. ANOVA is not solely a tool for statistical significance testing; in practice, it is widely applied for variance decomposition and uncertainty analysis (e.g. Rowell et al. (1995); Rowell, 1998; Kang and Shukla, 2006; Saha et al., 2016a, 2016b ). In fact, the calculation of the ‘signal-to-noise’ ratio is a well-established application that makes direct use of the ANOVA framework.

The term "sub-seasonal scale" is poorly defined. Although filtering bands (e.g., synoptic scale, MJO) are mentioned later, the temporal scope is not specified in key derivations, undermining the core argument that "sub-seasonal components are building blocks of seasonal predictions."

Reply: “Sub-seasonal scale”, i.e. synoptic (~2-7 days), super-synoptic (~10-20 days) and MISOs (~-20-60 days) are commonly used in literature. Section 2.3.3, in method describes comprehensively how co-variability between sub-seasonal and seasonal components are calculated. Line 142 “The sub-seasonal components (e.g., precipitation events on hourly, synoptic, or MISOs timescales) are the building blocks ...” clearly mentioned what we mean by sub-seasonal components. In the same section, we describe method to get these band explicitly in line 190-193: “Fourier analysis is used to construct a smooth annual cycle and total anomaly, while a Lanczos band-pass filter (Duchon, 1979) is applied to filter daily time series data into synoptic (2-5 days), super-synoptic (10-20 days), and Monsoon Intra-Seasonal Oscillations/Madden-Julian Oscillation(MISO/MJO) (20-60 days) bands.” However, we can add one or two lines in the beginning of method section 2.3.3 about sub-seasonal scale, which will make easy to understand for readers.

3. Internal Contradictions in the Theoretical Framework The manuscript contains fundamental academic flaws:

Conflict Between Orthogonality Assumption and Physical Reality: The derivation of Equation (11) assumes orthogonality (zero covariance) between different components. However, significant nonlinear interactions exist across scales (e.g., synoptic, MJO, external forcing) in the climate system, making this assumption physically unrealistic;

Reply: Strict orthogonality between components may not fully hold in the climate system due to non-linear cross-scale interactions (e.g., synoptic variability, MJO, external forcing). However, band-pass filtering minimizes covariance between frequency bands, reducing cross-scale leakage and allowing the total variance to be approximated as the sum of sub-seasonal components. Thus, the orthogonality assumption serves as a practical statistical simplification that enables robust variance partitioning and comparison across time scales, and it is widely adopted in climate diagnostics and prediction studies.

Logical Inconsistency Paradox: The orthogonality assumption directly contradicts the manuscript's central argument that "covariability between sub-seasonal components and seasonal anomalies is a source of error." A theoretical framework based on an unreliable assumption cannot effectively demonstrate the effects of non-orthogonality, rendering the overall argument untenable.

Reply : This interpretation is not correct. Here we argue that, since sub-seasonal components are the building blocks of the seasonal mean, their unrealistic contributions or systematically biased contributions reduces seasonal prediction skill and hence the predictability (evident in Figure 7a,b). The use of the orthogonality assumption is a methodological simplification to partition variance across time scales; it does not imply the absence of physical co-variability. The clear separation between lower- and higher-skill ensemble members (Figure 7), along with the larger biases in their covariance (particularly in the MISO bands), demonstrates that the orthogonality assumption is both useful and practical for understanding their influence on seasonal variability and predictability. Nevertheless, in the perfect model framework, it is assumed that only the initial error contributes to the noise/ensemble spread and hence the predictability, which overlook other sources of error such as physical processes, numerical methods etc. Moreover, it has no binding relationship with observations, thus often giving rise to paradoxical situation .

Conclusion Due to issues such as unclear presentation, conceptual confusion, and logical paradoxes, the conclusions of this manuscript are unreliable, and we recommend rejection.

We hope that we are able to address the concerns/comments. As mentioned in the point wise reply, brief discussion in some part of the manuscript may make it easier to follow for readers. 

Dear Prof. Scaife,

Please find the attached reply for your comments and suggestions. We have tried to address all of your major concerns through modification in the manuscript and additional analysis. Hope these will convince you.

Thanks a lot for your time and suggestions, which have helped to improve the manuscript.

Regards,

Subodh