This study introduces a methodology for constructing long-term, gridded surface cloud radiative kernels (GCF-CRKs) and estimating Arctic shortwave cloud radiative effects (SW CRE) using a fused cloud fraction (CF) dataset and CERES satellite observations. The authors claim their approach improves DSSR estimates and quantifies the spatiotemporal variability of CRE with greater accuracy, highlighting its application for refining climate models. 

My major concern is this manuscript may not be suitable for Earth System Science Data (ESSD) as it does not produce a comprehensive dataset for community use, focusing instead on methodological refinements. The paper does not meet the core requirements for ESSD as it lacks the breadth and generalizability necessary for community adoption. The dataset is limited in scope, focusing narrowly on CF without comprehensively incorporating critical variables such as cloud vertical structure, microphysics, or optical thickness, which are essential for accurately addressing the major uncertainties in CRE estimation. This omission undermines the dataset's ability to comprehensively address key scientific questions and limits its usability in broader climate modeling and research contexts. The study also lacks sufficient discussion of the broader scientific implications of its findings. 

Specific technical comments:

1. The dataset is narrowly tailored to CF-related analysis and lacks general applicability for broader climate research.

2. The reliance on cloud fraction alone fails to address key uncertainties in cloud radiative effects. Without incorporating vertical cloud structure, microphysics, and optical thickness, the methodology cannot fully resolve critical gaps in CRE estimation.

3. The manuscript lacks adequate justification for its claims of improved CRE estimation. The validation against independent datasets and robust comparisons in high-latitude regions remain insufficient.

4. The broader scientific implications of the findings are not thoroughly explored. The relevance of these results to Arctic amplification and global climate feedback is understated and lacks context.

Overview:

In this paper, the authors developed a set of computationally efficient, long-term gridded surface cloud fraction radiative kernels (GCF-CRKs) to estimate cloud radiative effect in polar regions. The kernels reflect the climatological cloud properties (especially for cloud optical thickness) in the Arctic regions, so the accuracy of these kernels on downwelling surface shortwave radiation is good under current climate conditions. However, the climatological cloud properties are changing under global warming, so it is uncertain whether the GCF-CRKs still works under climate change.

Although the method might be useful for climate studies, the limitations of this method have not been well addressed, so major revisions are required.

Specific comments:

1. The downwelling surface shortwave radiation is most sensible to cloud optical thickness. As the climate warms, the average optical thickness of clouds changes due to changes in cloud phase and water content, so the GCF-CRKs derived from current climate would be less accurate in future climate. This is an important limitation, and should be discussed in the paper.
2. Cloud masking effect should be removed when the kernel results are compared to observations. (Soden et al., 2008)
3. An advantage of GCF-CRK is that it avoids the uncertainty induced by cloud optical property retrievals. Theoretically, CRKs should be more accurate than GCF-CRKs if the cloud property products were accurate. In reality, the cloud properties retrieved in Arctic regions have large uncertainties due to large surface reflectivity and large solar zenith angle, that’s why GCF-CRK results is better than CRK results in some Arctic regions.

This paper introduces a long-term gridded surface cloud fraction radiative kernels (GCF-CRKs) by leveraging the correlation between the TOA shortwave radiative parameters and surface radiation, combined with fused cloud fraction datasets from multiple satellite sources. Based on this kernel, the authors isolate the cloud radiative effect and corrected the downwelling surface shortwave radiation bias caused by cloud fractions. It is known that there are large uncertainties in cloud radiative effect derived from satellite observations in the Arctic region. The study used high quality cloud fractions data to quantify this effect and makes an important contribution. The manuscript is organized and well written. I recommend to accept this manuscript subject to minor but necessary revisions.

General comment:

1. There are many cloud parameters that contribute to cloud radiative effects, such as cloud optical thickness, effective radius of cloud particles, and others. This manuscript selects cloud fraction as the primary variable. Please discuss the rationale behind this choice and the feasibility of extending the study to include other variables in the future.
2. The validation against independent datasets and robust comparisons in high-latitude regions should be emphasized.
3. Enhance the discussion section of the paper by integrating current hot topics in climate change, such as the Arctic amplification effect. Elaborate on the potential contributions of this study's findings to understanding global climate feedback mechanisms and polar climate change.