Heatwaves are expected to increase with climate change, posing a significant threat to population health. In India, with the world's largest population, heatwaves occur annually but have not been comprehensively studied. Accordingly, we evaluated the association between heatwaves and all-cause mortality and quantifying the attributable mortality fraction in India. We obtained all-cause mortality counts for ten cities in India (2008-2019) and estimated daily mean temperatures from satellite data. Our main extreme heatwave was defined as two-consecutive days with an intensity above the 97th annual percentile. We estimated city-specific heatwave associations through generalised additive Poisson regression models, and meta-analysed the associations. We reported effects as the percentage change in daily mortality, with 95% confidence intervals (CI), comparing heatwave vs non-heatwave days. We further evaluated heatwaves using different percentiles (95th, 97th, 99th) for one, two, three and five-consecutive days. We also evaluated the influence of heatwave duration, intensity and timing in the summer season on heatwave mortality, and estimated the number of heatwave-related deaths. Among ∼ 3.6 million deaths, we observed that temperatures above 97th percentile for 2-consecutive days was associated with a 14.7 % (95 %CI, 10.3; 19.3) increase in daily mortality. Alternative heatwave definitions with higher percentiles and longer duration resulted in stronger relative risks. Furthermore, we observed stronger associations between heatwaves and mortality with higher heatwave intensity. We estimated that around 1116 deaths annually (95 %CI, 861; 1361) were attributed to heatwaves. Shorter and less intense definitions of heatwaves resulted in a higher estimated burden of heatwave-related deaths. We found strong evidence of heatwave impacts on daily mortality. Longer and more intense heatwaves were linked to an increased mortality risk, however, resulted in a lower burden of heatwave-related deaths. Both definitions and the burden associated with each heatwave definition should be incorporated into planning and decision-making processes for policymakers.

Heat wave (HW) and Severe Heat Wave (SHW) events are the manifestations of extreme temperature causing an array of impacts on health, ecosystem, and economy. Since the mid‐20^th century, an increasing trend in the characteristics of heat waves has been observed over India causing an increased rate in human mortality. Our study aimed to analyze monthly, seasonal & decadal variations along with long‐term trends of HW and SHW events for pre‐monsoon (March‐May) and early summer monsoon (June‐July) season during 1951‐2016. HW and SHW events were identified using revised criteria given by India Meteorological Department (IMD) using daily gridded maximum temperature data at 0.5° x 0.5° resolution from IMD. The study found a Spatio‐temporal shift in the occurrence of HW events with a significantly increasing trend in three prominent heat wave prone regions i.e., Northwestern, Central and South‐Central India, highest being in West Madhya Pradesh (0.80 events/year), while a significantly decreasing trend was observed over eastern region i.e., Gangetic West Bengal (‐0.13events/year). SHW events showed a southward expansion and a spatial surge during the decades of 2001‐2010 and 2010‐2016. Tri‐decadal comparative assessment shows a decadal increase of around 12 HW and 5 SHW events post the 1980s. State‐wise Pearson's correlation between HW/SHW events and observed mortality reveals that the eastern coastal states i.e., Odisha and Andhra Pradesh show a significant positive correlation of 0.62 and 0.73 respectively. This significantly increasing trend in HW and SHW events may pose a grave risk to human health, predominantly on the vulnerable sections of the society. Heat waves need to be recognized as a potential health risk and demand further study, robust preparedness, and policy intervention.

South Asia is vulnerable to a variety of hydrometeorological hazards, which are often cross-boundary in nature. Climate change is expected to influence many of these hazards. Thus, climate-related risks over South Asia make disaster risk reduction (DRR) and climate change adaptation (CCA) key policy goals. Recently there is an increasing consensus that DRR including CCA should be embedded in development planning. Disaster risk reduction including CCA has progressively gained importance in global governance. Across South Asia, however, such integration is only in a preliminary stage. This review was to assess the existing status and scope of DRR including CCA in development projects across South Asia, so that an effective and achievable deliberation may be made to regional policymakers. A total of 371 projects relevant to CCA and DRR were reviewed. The project inventory was diverse in nature with respect to location, scale, sectoral focus, and strategic importance. Bangladesh, India, and Bhutan were observed to be proactive in implementing DRR- and CCA-related projects. Meta-analysis of the project inventory suggests an urgent need for an individual and collaborative convergence of processes for DRR and CCA through policies, plans, strategies, and programs.

Regional climate models (RCMs) are routinely applied for regional climate assessments. The RCM simulated rainfall typically overpredict the light rain/drizzle events. To correct the typical errors noted in RCM rainfall simulations, in this study, three bias‐correction methods: linear scaling (SCL), local intensity scaling (LOCI), and empirical quantile mapping (EQM), have been employed. These methods are used to correct monsoon rainfall simulations from 7 RCMs across 14 agroclimatic zones (ACZs) in India from 1970 to 2005. The corrected rainfall data were compared to the observations obtained from India Meteorological Department. The performance of the three methods was assessed using: probability distribution function, consecutive dry day index (CDD), R95 (rainfall distribution at 95th percentile), and spatial correlation. The results vary spatially across the different ACZs. Overall, SCL method is more effective followed by EQM while LOCI was relatively less effective in correcting the errors. Spatial analysis of the rainfall indicates notable improvements over the Western Himalayan Region, which has a complex topography and land use. Error metricsreveal broad improvements across different ACZs, except for Central Plateau Hill Region, East Coast Plain Hill Region, and Southern Plateau Hill Region. The SCL and EQM perform well, and the results are particularly good for simulating wet days (R95), while no distinct variation was found among correction methods to reduce dry bias (CDD). The results provide ACZ and region‐specific utilization of an effective bias correction technique for impact assessment studies in the Indian monsoon region.