Climate Dynamics

, Volume 51, Issue 1–2, pp 1–15 | Cite as

Future projections of Indian summer monsoon rainfall extremes over India with statistical downscaling and its consistency with observed characteristics

  • Kulkarni Shashikanth
  • Subimal Ghosh
  • Vittal H
  • Subhankar Karmakar


Indian summer monsoon rainfall extremes and their changing characteristics under global warming have remained a potential area of research and a topic of scientific debate over the last decade. This partially attributes to multiple definitions of extremes reported in the past studies and poor understanding of the changing processes associated with extremes. The later one results into poor simulation of extremes by coarse resolution General Circulation Models under increased greenhouse gas emission which further deteriorates due to inadequate representation of monsoon processes in the models. Here we use transfer function based statistical downscaling model with non-parametric kernel regression for the projection of extremes and find such conventional regional modeling fails to simulate rainfall extremes over India. In this conjuncture, we modify the downscaling algorithm by applying a robust regression to the gridded extreme rainfall events. We observe, inclusion of robust regression to the downscaling algorithm improves the historical simulation of rainfall extremes at a 0.25° spatial resolution, as evaluated based on classical extreme value theory methods, viz., block maxima and peak over threshold. The future projections of extremes during 2081–2100, obtained with the developed algorithm show no change to slight increase in the spatial mean of extremes with dominance of spatial heterogeneity. These changing characteristics in future are consistent with the observed recent changes in extremes over India. The proposed methodology will be useful for assessing the impacts of climate change on extremes; specifically while spatially mapping the risk to rainfall extremes over India.


Statistical downscaling Extreme value theory (EVT) Climate change 



We acknowledge the World Climate Research Programme’s working Group on coupled Modelling, which is responsible for CMIP, and we thank the modeling groups for producing and making available their model output. For CMIP the U.S. Department of Energy’s Program for Climate Model Diagnosis and Intercomparison (PCMDI) provides coordinating support and led development of software infrastructure in partnership with the Global Organization for Earth System Science Portals. We also would like to thank APHRODITE, Japan for making available observed data.

Supplementary material

382_2017_3604_MOESM1_ESM.docx (4.7 mb)
Supplementary material 1 (DOCX 4789 KB)


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© Springer-Verlag Berlin Heidelberg 2017

Authors and Affiliations

  1. 1.Department of Civil EngineeringIndian Institute of Technology BombayMumbaiIndia
  2. 2.Interdisciplinary Program in Climate StudiesIndian Institute of Technology BombayMumbaiIndia
  3. 3.Center for Environmental Science and EngineeringIndian Institute of Technology BombayMumbaiIndia
  4. 4.Department of Civil Engineering, University College of EngineeringOsmania UniversityHyderabadIndia

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