Recent exacerbation of extreme precipitation events (EPEs) and related massive disasters in western Himalayas (WH) underpins the influence of climate change. Such events introduce significant losses to life, infrastructure, agriculture, in turn the country’s economy. This chapter provides an assessment of long-term (1979–2020) as well as recent changes (2000–2020) in precipitation extremes over WH for summer (JJAS) and winter (DJF) seasons. Different high-resolution multi-source climate datasets have been utilized to compute the spatiotemporal trends in intensity and frequency of EPEs. The hotspots of rising extremes over the region have been quantified using the percentile approach where daily precipitation exceeds the 95th percentile threshold at a given grid. The findings reveal geographically heterogeneous trends among different datasets; however, precipitation intensity and frequency show enhancement both spatially and temporally (though insignificant). For both seasons, dynamic and thermodynamic parameters highlight the role of increased air temperatures and, as a result, available moisture in the atmosphere, signifying the consequences of global warming. Rising precipitation extremes in summer are sustained by enhanced moisture supply combined with increased instability and updraft, due to orography, in the atmosphere whereas winter atmosphere is observing an increase in baroclinicity, available kinetic energy, vertical shear and instability, contributing to a rise in precipitation extremes.
<p>The South Asian monsoon is a lifeline of over two billion inhabitants of the Indian subcontinent. Hence, a reliable monsoon prediction system is essential for the operation of weather and climate over the region. The state-of-art General Circulation Models (GCMs) are powerful tools for monsoon prediction and assessing the effects of climate change on precipitation and temperature in rising extreme events such as floods, storms, heatwaves, and drought. However, selecting appropriate GCMs is a grand challenge for assessing climate change projections due to their significant uncertainties. The present study will evaluate the relative performance of GCMs of phases 5 and 6 of the Coupled Model Intercomparison Project dataset based on their multi-model mean (MMM) ability to project rainfall and temperature during the summer season (JJAS) over central India. In addition to the spatial patterns under the Shared Socioeconomic Pathways (SSPs), the study will also examine the model's ability to simulate interannual variability. The present research aims to determine the most reliable CMIP6 and CMIP5 datasets model and their comparison in simulation and projection of seasonal temperature and precipitation. The seasonal climatological mean of GCMs simulated rainfall and temperature shows variability at different scales over central India. CMIP6 multi-model mean demonstrate a reasonably well performance than CMIP5 in the seasonal mean cycle simulation with a better representation of the rainfall. The present study will also investigate the changes in sources of projection uncertainty and future precipitation indices. Finally, the current research will discuss the highlights of comparing the CMIP6 and CMIP5 datasets and<strong>&#160;</strong>their representations of better simulation performances based on the skill score metrics of precipitation and temperature indices.</p><p><strong>KEYWORDS</strong>. CMIP6, CMIP5, MMM precipitation and temperature, Projection</p>
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