The peak structure of the extreme precipitation-temperature scaling curve has been documented by many studies. However, the causes for the peak structure are not understood well. The authors explain this phenomenon from the perspective of the radiative effect of cloud on surface temperature. When the cloud effect is removed, extreme precipitation continuously increases with surface temperature without a break. The authors conclude that the cooling effect of cloud during precipitation dominates the negative scaling at higher temperature. The manuscript is very helpful to better understanding the underlying scheme for the peak structure of the relationship between extreme precipitation and temperature. I recommend accepting for publication with a minor revision. The detailed comments are provided below:

Lines 232-235, Figure 4b: the differences between the two temperatures are positive in most regions. However, “clear sky” temperature is lower “all sky” temperature in the northernmost part (red). Can authors explain why?

The modeled monthly temperature is evaluated in section 3.1. However, the daily temperature is used for the precipitation-temperature scaling. How good is the daily temperature calculated from equation (7)?

The radiative effect of cloud on surface temperature can explain the breakdown of the P-T scaling curve. Can authors provide us with the observation-based cloud information to further validate the statement? For example, the map of cloud over.

Extreme precipitation increases monotonically with temperature when the cloud cooling effect is removed. Is there any implication for the future prediction of extreme precipitation based on the monotonical P-T relationship?

Minor comments:

Line numbers are not continuous.

Line 100: the equation (1) -> equation (1)

Line 121: “Detailed derivation about the same can be found in ……”. I guess that “same” here is a typo.

Line 201: following -> aforementioned

Figure 1: the symbols used for the flux-type variables in the figure are not consistent with the symbols in the text.

Figure 2: in the figure caption, (a) is missing, and (B) and (C) should be lower cases. In addition, 2003 should be placed on the beginning the curve. Otherwise, it gives a wrong impression that the data before 2003 is used in this study.

Appendix A: observed (yellow) and “all-sky” (red) temperatures are mentioned. However, there is no “all-sky” temperature in Figure A1.

Figure A1: it is better to use the actual date instead of days for x-axis in (a), (b), and (c). As such, one can see the seasonal variation of precipitation from the figure more clearly.

The scaling of extreme precipitation-temperature is not always consistent with Clausius-Clapeyron scaling rate. The reasons why the thermodynamic CC rate does not restraint the scaling of extreme precipitation-temperature is a challenge to understand the change in extreme precipitation under the global warming. This article offers a new perspective to resolve the negative precipitation-temperature scaling in India. The comments are as following:  

1. During the period winter monsoon, the precipitation-temperature scaling has a drop over the western India after removing the radiative effects of clouds (Figure C2). The results may imply that the scaling of extreme precipitation-temperature is not constrained by the radiative effects of cloud. Then, the generalizability of the conclusions revealed by this study may be questionable. Therefore, more explain or discussion about the drop scaling of P-T during the period of winter monsoon are essential.

1. South of India, the cloud-driven cooling does not play a role in the extreme precipitation-temperature scaling (Figure 6 and B1). This phenomenon needs further explanation by combined other factors (e.g., the topography, distributions of sea and land).

1. The scaling of precipitation-temperature after removing the cloud cooling effects are not equal CC rate (~7%/°C) strictly, the effects of other factors that could influence the scaling should be discussed in Section 4.

1. The Figure 1 given the temperature after removing the cloud effects at monthly scale. However, this study did not involve the scaling of precipitation-temperature at monthly scale, so the Figure could be replaced by daily data.

The present paper explains the break in Clausius-Clapeyron scaling rate in India through use of observation and a surface energy balance approach constrained by thermodynamic. The authors have made well reasoning and organization of the contents with minor faults, so I would recommend minor revision before publication. The comments are as follows:

1. In Figure 1, the monthly temperature is used despite of the daily scale for analysis. This may need to be fixed.
2. There seems to be minor difference between the clear sky scaling in IMD and TRMM in foothill of Himalayas north of India in the figure 6, does the authors know why?
3. On figure 6, while the removal of the effect in the north-west India in IMD (Fig. 6c) is relatively similar compared to the Observed, the TRMM data (Fig. 6e) exhibits more cooling, does the authors know why? Maybe it’s because TRMM are more affected by clouds covers?
4. In the introduction, the authors used the “here we ….”, which seems pretty weird, this is in Line 57, 64, 83. It may be better if the authors use the expression such as “In this study” or words of similar sorts.