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or the change in the spatio-temporal distribution of precipitation extremes between the period 1930-1970 and 1971-2017. These periods are selected based on the climate regime shift in the 1970s. A jump in the mean and 95 th percentile of the daily annual precipitation is also noticed over India in the decade of 1970-1980 (please refer to the detailed discussion in the supplementary document). During the study of future precipitation, the areas affected by ISM are included in the study domain. Future precipitation extremes are analyzed using 7 Coordinated Regional Climate Downscaling Experiment (CORDEX) simulations to investigate -whether the behavior of precipitation extremes (their magnitude and spatial pattern) will be similar and, if yes, then what the possible causes are. The details of CORDEX models used in this study are provided in Table S1 (supplementary document). Three characteristics of annual daily precipitation, namely mean precipitation, and threshold (cut-off) and mean precipitation for days with extreme precipitation, are analyzed for their spatio-temporal changes. Two different thresholds (i.e., 95 th and 99 th percentile; henceforth represented as P95 and P99 respectively) are selected to identify the days with extreme precipitation. The mean of extreme precipitation magnitude equal to or greater than P95 and P99, are denoted by M95 and M99, respectively. Further, the reasons for the changes in the aforementioned precipitation characteristics are investigated by analyzing the air temperature, moisture flux, and moisture convergence from the observed data and future CORDEX simulations. Scientific RepoRtS |(2020) 10:6452 | https://doi.

Changes in extreme precipitation due to climate change often require the application of methods to bias correct simulated atmospheric fields, including extremes. Most existing bias correction techniques (i) only focus on the bias in the mean value or on the extreme values separately, and (ii) exclude zero values from analysis, even though their presence is significant in daily precipitation. We developed a copula-based bias correction scheme that is suitable for zero-inflated daily precipitation data to correct the bias in mean as well as in extreme precipitation at any specific statistical quantile. In considering the whole of Germany as a test bed, the proposed scheme is found to work well across the entire study area, including the German Alpine regions. The joint distribution between observed and regional climate model (RCM)-derived precipitation is developed through copulas. In particular, the joint distribution is modified to make it discrete at zero in order to account for zero values. The benefit of considering zero precipitation values is revealed through the improved performance of bias correction both in the mean and extreme values. Second, the quantile that best captures the bias (whether in the mean or any extreme value) is determined for a specific location and varies spatially and seasonally. This relaxation in selecting the location-specific optimal quantile renders the proposed methodology spatially transferable. By acknowledging possible changes in extreme precipitation due to climate change, the proposed scheme is expected to be suitable for climate change impact assessments for extreme events worldwide.

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Abstract. In the present study we compare the MODIS (Moderate Resolution Imaging Spectroradiometer) derived aerosol optical depth (AOD) data with that obtained from operating sky-radiometer at a remote rural location in southern India (Gadanki, 13.45 • N, 79.18 • E) from April 2008 to March 2011. While the comparison between total (coarse mode + fine mode) AODs shows correlation coefficient (R) value of about 0.71 for Terra and 0.77 for Aqua, if one separates the AOD into fine and coarse mode, the comparison becomes very poor, particularly for fine mode with an R value of 0.44 for both Terra and Aqua. The coarse mode AOD derived from MODIS and sky-radiometer compare better with an R value of 0.74 for Terra and 0.66 for Aqua. The seasonal variation is also well captured by both ground-based and satellite measurements. It is shown that both the total AOD and fine mode AOD are significantly underestimated with slope of regression line 0.75 and 0.35 respectively, whereas the coarse mode AOD is overestimated with a slope value of 1.28 for Terra. Similar results are found for Aqua where the slope of the regression line for total AOD and fine mode AOD are 0.72 and 0.27 whereas 0.95 for coarse mode. The fine mode fraction derived from MODIS data is less than one-half of that derived from the sky-radiometer data. Based on these observations and comparison of single scattering albedo observed using sky-radiometer with that of MODIS aerosol models, we argue that the selection of aerosol types used in the MODIS retrieval algorithm may not be appropriate particularly in the case of southern India. Instead of selecting a moderately absorbing aerosol model (as being done currently in the MODIS retrieval) a more absorbing aerosol model could be a better fit for the fine mode aerosols, while reverse is true for the coarse mode aerosols, where instead of using "dust aerosols" which is relatively absorbing type, usage of coarse sea-salt particles which is less absorbing is more appropriate. However, not all the differences could be accounted based on aerosol model, other factors like errors in retrieval of surface reflectance may also be significant in causing underestimation of AOD by MODIS.