| Summary: | 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 Nino/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 (similar to 31%) and 249 (similar to 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.
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