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In the context of climate change, the widespread practice of directly employing Multi‐Model Ensembles (MMEs) for projecting future climate extremes, without prior evaluation of MME performance in historical periods, remains underexplored. This research addresses this gap through a comprehensive analysis of ensemble means derived from CMIP6‐based models, including both simple and weighted averages of precipitation (SEMP and WEMP) and temperature (SEMT and WEMT) time series, as well as simple (SEME) and weighted (WEME) averages of extremes based on model‐by‐model analysis. The study evaluates the efficacy of MMEs in capturing mean annual values of ETCCDI indices over India for the period 1951–2014, utilising the IMD gridded data set as a reference. The results reveal that SEME and WEME consistently align closely with IMD data across various precipitation indices. At the same time, SEMP and WEMP consistently display underestimation biases ranging from 20% to 80% across all precipitation indices, except for CWD, where there is an overestimation bias. Moreover, SEMP and WEMP consistently underestimate CDD and overestimate CWD, indicating a systematic bias in these ensemble means, while WEME and SEME demonstrate satisfactory performance. SEMT and WEMT exhibit notable underestimation in temperature indices. In summary, adopting SEME and SEMT leads to a more robust assessment of precipitation and temperature extremes, respectively. These findings highlight the limitations of traditional MME methodologies in reproducing observed extreme precipitation events across various climatic zones in India, offering essential insights for refining climate models and improving the reliability of climate projections specific to the Indian subcontinent. [ABSTRACT FROM AUTHOR]

Flash droughts, characterised by their rapid onset and significant impacts on local communities and agriculture, pose challenges for monitoring and mitigation efforts due to their unpredictable nature. Therefore, this study aims to investigate the occurrence, characteristics and influencing factors of flash droughts in the Ganga River Basin (GRB) for the period 1981–2020. Flash droughts are identified using the pentad averaged root zone soil moisture (PRZSM). The Mann‐Kendall trend test is used to determine the spatial and temporal pattern of flash drought characteristics. Furthermore, a multivariate flash drought index (MFDI) is developed to account for the combined effects of flash drought characteristics. Finally, wavelet coherence analysis evaluates the relationship between climatic oscillations and MFDI at the sub‐basin scale. Utilising a revised flash drought identification approach incorporating non‐stationary cumulative distribution functions (CDFs), the study identifies flash droughts in the GRB, particularly emphasising higher occurrences in the Chambal and Upper Yamuna Sub‐basins. Analysis of flash drought characteristics under stationary and non‐stationary conditions reveals increased frequency, severity and decline rates, highlighting the impact of evaporation and latent heat flux. Furthermore, the Upper Ganga Sub‐basin demonstrates coherence with the DMI at shorter time scales (1 to 4‐year time scales), while the Lower Ganga Sub‐basin displays a pronounced association with the NINO3.4 index (5.65‐year time scale), indicating the impact of climate oscillations on flash drought dynamics in these regions. These findings provide valuable insights for drought monitoring, prediction and management strategies in a changing climate, emphasising the need for integrated approaches to address the complex interplay between climate variability and flash drought occurrences in the GRB. [ABSTRACT FROM AUTHOR]

Adaptation and resilience practitioners lack guidance on how to understand and manage extreme climate risk using the data available. We present a methodological framework to integrate the satellite as well as location based data sets to estimate extreme climate risk. The framework, in detail, has been demonstration using a study carried out to quantify extreme rainfall risks in India incorporating the influence of global (large scale oscillations) as well as local factors (population, infrastructure, economic activity) in a probabilistic model. We use nonstationary extreme value theory along with Bayesian uncertainty analysis to model the time varying influence of oscillations such as El Niño/Southern Oscillation, Indian Ocean Dipole, and North Atlantic Oscillation in augmenting high rainfall risks in 637 districts across 29 states of India. It is found that at least 50% of the districts in 8 out of 29 states are at high risk. Extreme risk is observed in 198 (~31%) and 249 (~39%) districts caused by heavy downpour and extremely long wet spells, respectively. This study provides a framework to identify local implications of global factors and is aimed at supporting policy makers in framing extreme rainfall‐induced disaster risk reduction strategies. [ABSTRACT FROM AUTHOR]