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The frequency and intensity of extreme thermal stress conditions during summer are expected to increase due to climate change. This study examines sixteen models from the Coupled Model Intercomparison Project Phase 6 (CMIP6) that have been bias-adjusted using the quantile delta mapping method. These models provide Universal Thermal Climate Index (UTCI) for summer seasons between 1979 and 2010, which are regridded to a similar spatial grid as ERA5-HEAT (available at 0.25° × 0.25° spatial resolution) using bilinear interpolation. The evaluation compares the summertime climatology and trends of the CMIP6 multi-model ensemble (MME) mean UTCI with ERA5 data, focusing on a regional hotspot in northwest India (NWI). The Pattern Correlation Coefficient (between CMIP6 models and ERA5) values exceeding 0.9 were employed to derive the MME mean of UTCI, which was subsequently used to analyze the climatology and trends of UTCI in the CMIP6 models.The spatial climatological mean of CMIP6 MME UTCI demonstrates significant thermal stress over the NWI region, similar to ERA5. Both ERA5 and CMIP6 MME UTCI show a rising trend in thermal stress conditions over NWI. The temporal variation analysis reveals that NWI experiences higher thermal stress during the summer compared to the rest of India. The number of thermal stress days is also increasing in NWI and major Indian cities according to ERA5 and CMIP6 MME. Future climate projections under different scenarios (SSP1-2.6, SSP2-4.5, and SSP5-8.5) indicate an increasing trend in thermal discomfort conditions throughout the twenty-first century. The projected rates of increase are approximately 0.09 °C per decade, 0.26 °C per decade, and 0.56 °C per decade, respectively. Assessing the near (2022–2059) and far (2060–2100) future, all three scenarios suggest a rise in intense heat stress days (UTCI > 38 °C) in NWI. Notably, the CMIP6 models predict that NWI could reach deadly levels of heat stress under the high-emission (SSP5-8.5) scenario. The findings underscore the urgency of addressing climate change and its potential impacts on human well-being and socio-economic sectors. [ABSTRACT FROM AUTHOR]

Drought complexity may not be accurately characterized by univariate meteorological or hydrological drought indices under the intensification of hydrological cycle due to climate change and human activities. In particular, such drought indices require long series of hydro-meteorological data, which are unavailable over ungauged and data-scarce catchments. In this study, a multivariate drought index, Standardized Precipitation Actual Evapotranspiration Index-Agro (SPAEI-Agro), is proposed, which combines meteorological and hydrological variables as precipitation (P), actual evapotranspiration (AET), runoff and groundwater (GW) of ungauged catchments and sub-catchment scales with diverse climatology. SPAEI-Agro was able to characterize severe drought events more accurately in humid and dry sub-humid sub-catchments compared to Standardized Precipitation Evapotranspiration Index (SPEI) and Standardized Streamflow Drought Index (SSDI) for Tunga-Bhadra River, India. SPEI (based upon potential evapotranspiration) and SSDI (based on streamflow) showed intensified drought characteristics compared to the new P, AET and GW-based drought indicator SPAEI-Agro in semi-arid and dry sub-humid climates. [ABSTRACT FROM AUTHOR]

Intensification of hydrologic cycle, and consequence rise of intense short-term precipitation, are considered as the manifestations of climate change. This may lead to an alteration in Intensity–Duration–Frequency (IDF) relationship that may change other hydrological processes as well. The IDF relationship also serves as a crucial information for the design of any water infrastructure. This study investigates the spatiotemporal changes in IDF relationship involving hourly precipitation events between past and future climate at various return periods across India that spans over a wide range of climatology. Contrast between historical (1979–2014), using two reanalysis data, and future periods (immediate future: 2015–2039, near-future: 2040–2059 and far-future: 2060–2100) is explored along with its spatial (re-) distribution. The future simulations of precipitation are derived from three climate models, participating in 6th phase of Coupled Model Intercomparison Project (CMIP6), for three shared socio-economic pathways (SSPs), i.e., SSP126, SSP245 and SSP585. The results show that almost entire Indian mainland will experience an increase (~41–44%) in the hourly precipitation intensity under the worst climate change scenario (SSP585) with a return period as low as 2 years (almost a regular incidence). Furthermore, even under a moderate climate change scenario (SSP245), almost entire Indian mainland (~82–99% of spatial extent) will be affected from a significant increase (on an average 19%) in the hourly precipitation intensity. It is true for higher return periods as well. Findings of the study are alarming for many water infrastructures. This study develops new set of IDF curves across India considering a changing climate that will be useful to set a revised design criteria for water infrastructure. [ABSTRACT FROM AUTHOR]

This study, for the first time, investigates the physical association between Predecessor Rain Events (PREs) and peak runoff generation in seven catchments over the Upper Mahanadi River basin (UMRB), India. A statistical–dynamical framework is developed to assess the compounding impact of PREs (as preconditioned events) versus riverine floods during both retrospective and projected climate. Based on models' fidelity to capture historical climatology, we select four out of nine Global Climate Models (GCMs) during historical (1980–2005), and the three projected time windows, that is, near future (2010–2039), mid‐century (2040–2069), and the far future (2070–2099) planning horizons with RCP8.5 emission scenario. We assess changes in compound flood hazards in historical versus projected periods using a newly proposed Bivariate Hazard Ratio (BHR) index, which represents the ratio between bivariate return periods during the projected versus the historical time windows. Assessing bivariate return periods (characterized by 'AND' operator) of rainfall‐driven compound floods shows decreased flood hazard in the mid‐century and far‐future planning horizons. Accounting ranges of uncertainty from climate model simulations and the propagation of uncertainty across the numerical model chain, overall we show considering PRE as the covariate, floods in larger catchments show an increase in compound flood hazard in the projected period. [ABSTRACT FROM AUTHOR]

The complex relationship between climate knowledge production and political action has long been discussed. Critical and post-colonial analyses have looked at how models, used to produce scientific projections of climatic shifts, produce novel concepts of territory by the nation-state. The production of novel political temporalities has largely been ignored, however. This chapter critically analyses and compares two climate reports produced by the Indian Government. These reports, the first climatology reports developed in India, represent two significant shifts in the State's temporal imaginary of climate change and climate politics. First, the State, through these reports, temporally orients itself to the future (models of potential climates-to-be), rather than the past (attribution of historical responsibility to Northern states) as it had during the first half of international climate negotiations at the United Nations. Second, by focusing on the development of regional-specific models, the Indian government subtly continues to resist the imposition of a synchronized, global time of climate change, albeit through a nationalist framework. If the meaning of climate change is ultimately derived from a clash of competing temporalities between Global South and North, as well as between humanity and Nature, it speaks to the necessity of post-colonial scholars to engage directly with the meanings of the future in the Anthropocene. [ABSTRACT FROM AUTHOR]

High-resolution regional climate model (RCM) simulations are found to be very useful in deriving realistic climate change projection information. This study uses high-resolution dynamical downscaling framework (CCSM4-WRF) for India. To delineate the advantage of high resolution, we compared the results of 9-km resolution CCSM4-WRF simulations against the 50-km resolution RCM simulations under Coordinated Regional Climate Downscaling Experiment-South Asia (CORDEX-SA) program. Quantitative estimations show that majority of CORDEX-SA models exhibit large dry bias (< − 4 mm/day) and low pattern correlation coefficient (PCC) over the Western Ghats (WG). Mean climatology of Indian summer monsoon (ISM) rainfall simulated by high-resolution CCSM4-WRF outperforms the CORDEX-SA RCMs with low negative biases (~ 1 mm/day) and high PCC (≥ 0.755). This skill of CCSM4-WRF provides better confidence in its future projection at local scale. CCSM4-WRF projects future intensification of monsoon rainfall over most parts of India and reduction over southern WG, which is consistent with recent observed trends, but none of the CORDEX-SA RCMs could simulate this rainfall reduction. For all-India rainfall, ensemble mean of CORDEX-SA models projects an increase by 1.3 ± 0.9 mm/day and CCSM4-WRF projects 0.67 mm/day. Projected changes in socioeconomic variables such as population and gross domestic product (GDP) exhibit future enhancement over most parts of India but with spatial heterogeneity. Shared socioeconomic pathways scenarios show pronounced future population growth over Indian coastal areas and large enhancement in productivity over urban areas. Therefore, climate change projection information of ISM rainfall, together with enhanced future population and GDP, is useful for taking necessary steps for adaptation and mitigation in a sustainable manner. [ABSTRACT FROM AUTHOR]