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To simulate the extreme precipitation events through GCMs has become a challenge due to discrepancies in spatio-temporal resolution, physics, and parameterization schemes of the models along with deficiencies in the observed datasets. In this study, the performance of 27 CMIP6 models and their Multi model mean (MMM) in simulating extreme precipitation indices has been compared to the observed precipitation datasets (APHRODITE and IMD) over India during JJAS for 1975-2014. Meanwhile, the MMM shows a close agreement in simulating the indices derived from APHRODITE with PCC >0.6 for all indices with higher skill score (0.54), lower NRMSE than IMD. However, the MMM over- (under)-estimate the number of consecutive wet days (total precipitation) with a median relative error of 64% and 160% (5% and 20%) respectively, as compared to APHRODITE and IMD. Which inferred that similar biases still persist in the newly released CMIP6 GCMs with inter-observation dissimilarity in reproducing the indices. In general, the MMM is unable to replicate the very heavy precipitation (R20mm), with negative median relative errors. However, for all three aforementioned precipitation indices the extent of over- and under-estimation is less while comparing against the APHRODITE than IMD. For consecutive dry days (CDD), the MMM over- (under)-estimate over the North west (northern tip and peninsular as well as lee side of Western Ghat) parts of India, where the biases relative to APHRODITE (IMD) is large (less). The MMM simulates precipitation indices well, instead of using individual model. Whereas, the variation of NRMSE values of individual models are less with the exception of CDD and CWD, where the disagreement between the models with observation is large with larger interquartile model range. Comparing the relative errors between the different homogenous regions of India, all the regions are marginally performing good in simulating the different indices except the NW region, which is appended with larger relative error. It was worth noting that the models having higher spatial resolutions simulate the indices realistically with high (low) PCC (NRMSE), whereas the reversal is not valid for the worst performing models. Key Words: Extreme Precipitation, CMIP6, MMM, IMD, APHRODITE

The performance of sub-seasonal to seasonal prediction models, particularly outlining the role of the stratosphere in representing the surface climate viz. precipitation and temperature associated with the Indian Summer Monsoon (ISM), has been examined in this study. The hindcast data from two configurations of a fully coupled model part of the UK Met Office seasonal prediction system that differ only in vertical resolution namely Glosea4 L38 (GL38, Low Top) and Glosea4 L85 (GL85, High Top) have been used. In addition to this, the hindcast data from the updated version of the model i.e. Glosea5 (GL5) is also analyzed, which resembles the GL85 in case of vertical resolution thereby including an exclusively well-resolved stratosphere (unlike GL38) but with finer horizontal resolution than the later. It has been found that the GL85 is performing much better by eliminating the dry bias, particularly over the central Indian region as compared to the GL38 and GL5. The same implications are seen in the inter-annual variabilities produced by the models as GL85 is showing better results and closer to the observation in reproducing interannual variability of both precipitation and temperature. A large part of the inter-annual variations can be explained by the internal variability of the models but other important modes of inter-annual variability are also needed to explain the noted year-to-year fluctuations in these models. The impact of resolving the stratosphere on the temperature is not significant, as both GL38 and GL85 are producing similar biases over the ISM domain, and overall GL5 is showing better results. Furthermore, the influence of resolving stratosphere in representing surface climate by two versions of a CMIP5 model, CMCC-CM (Low Top) and CMCC-CMS (High Top) is also examined, and the improvement has been observed in the case of the high top model. Moreover, the circulation associated with the ISM for the models has also been analyzed to relate the model performance in reproducing the precipitation. The Somali jet is stronger in the high-top models leading to more moisture transport and convergence over the Indian land. In contrast, the Somali jet is shifted southwards and weaker in low-top version leading to more rainfall over the equatorial Indian Ocean and relatively less over India. The increase in vertical resolution (from GL38 to GL85) yields good results in representing precipitation, however, the increase in horizontal resolution (from GL85 to GL5) keeping the vertical resolution same has not been useful as it leads to the drier bias over the region. Keyword: sub-seasonal prediction to seasonal, hindcast, High Top/Low Top, Indian Summer Monsoon

The performance of climate models and their multi-model mean (MMM) in CMIP6 and CMIP5 precipitation simulations is compared using the India Meteorological Department (IMD) observations over the Indian summer monsoon (ISM) season (June-September) from 1975-2005. It is found that CMIP6 MMM better simulates the spatial distribution of seasonal mean precipitation than CMIP5 MMM and has shown improvements in biases over central India, northeast India, and also in capturing orography related precipitation over western Ghats and the northeast Himalayas. CMIP6 MMM performed better than CMIP5 MMM in capturing precipitation trends but failed to capture the overall declining trend. In consequence, in terms of precipitation simulation, the CMIP6 models (pattern correlation 0.4-0.8) outperformed the CMIP5 models (pattern correlation 0.2-0.7). MMMs well capture the observed phase of the annual cycle of the precipitation but underestimate the amplitude during summer monsoon months. In contrast, most of the CMIP6 models and their MMM have improved skill scores (SS) (SS 0.66) in reproducing the climatological summer precipitation compared to CMIP5 models and their MMM (SS 0.57). Furthermore, the results show that the Somali jet strength is well associated with ISM rainfall and has risen by about 2 m s-1 in CMIP6 MMM compared to CMIP5 MMM. Key Word: Indian Summer Monson, MMM, CMIP6, CMIP5, Precipitation