Due to the advancement in satellite and remote sensing technologies, a number of satellite precipitation products (SPPs) are easily accessible online at free of cost. These precipitation products have a huge potential for hydro-meteorological applications in data-scare catchments. However, the use of such products is still limited owing to their lack of accuracy in capturing the ground truth. To improve the accuracy of these products, we have developed a quantile based Bayesian model averaging (QBMA) approach to merge the satellite precipitation products. QBMA is a probabilistic approach to assign optimal weights to the SPPs depending on their relative performances. The QBMA approach is compared with simple model averaging and one outlier removed. TRMM, PERSIANN-CDR, CMORPH products were experimented for QBMA merging during the monsoon season over India’s coastal Vamsadhara river basin. QBMA optimal weights were trained using 2001 to 2013 daily monsoon rainfall data and validated for 2014 to 2018. Results indicated that QBMA approach with bias corrected precipitation inputs outperformed the other merging methods. On monthly evaluation, it is observed that all the products perform better during July and September than that in June and August. The QBMA approaches do not have any significant improvement over the SMA approach in terms of POD. However, the bias-corrected QBMA products have lower FAR. The developed QBMA approach with bias-corrected inputs outperforms the IMERG product in terms of RMSE.

Understanding spatiotemporal patterns and trends of Indian Summer Monsoon extremes have always been an important task mostly because the impacts of extreme events have an enormous effect on agriculture, economy, life and eco-system. In general, findings from the exploration of extreme events with limited data will have high uncertainty, and it is important to investigate the trends with a high long-term dataset for improved understanding of extreme events. Further, the patterns and interactions become further unusual, unexpected and unpredictable, coupled with the existing challenges of global warming-induced climate change. Hence, the study was primarily prompted by these realizations and an implied aspiration to quantify spatiotemporal patterns and trends of Indian Summer Monsoon extremes. In the present study, Extreme rainfall is defined in three categories, namely severe (99th percentile), very high (95th percentile) and high (90th percentile) during the monsoon season in each year. The temporal changes in extreme rainfall have been detected over the period 1901–2019 using non-parametric Mann-Kendall and Sen’s slope estimator tests, respectively. The analysis revealed significantly intensifying rainfall magnitudes in Jammu and Kashmir, parts of Gujarat, Rajasthan and Peninsular India. The outcomes indicate that the magnitudes of extreme rainfall are likely to increase in future in these regions. On the contrary, Central and some regions of North-eastern India shows deceasing trends in the extreme rainfall significantly. Consistency of the trends, both temporally and spatially has been explored considering three time windows with an overlap of 10 years. The findings of the temporal evolution of extreme rainfall reveal spatiotemporal pattern is not consistent in different periods of the study. The results of the present study will provide an improved understanding of spatiotemporal patterns of the daily rainfall extremes during the Indian summer monsoon.