Climatic Change

, Volume 125, Issue 1, pp 39–51 | Cite as

Changes in extremes and hydroclimatic regimes in the CREMA ensemble projections

  • Filippo Giorgi
  • Erika Coppola
  • Francesca Raffaele
  • Gulilat Tefera Diro
  • Ramon Fuentes-Franco
  • Graziano Giuliani
  • Ashu Mamgain
  • Marta Pereira Llopart
  • Laura Mariotti
  • Csaba Torma


We analyze changes of four extreme hydroclimatic indices in the RCP8.5 projections of the Phase I CREMA experiment, which includes 21st century projections over 5 CORDEX domains (Africa, Central America, South America, South Asia, Mediterranean) with the ICTP regional model RegCM4 driven by three CMIP5 global models. The indices are: Heat Wave Day Index (HWD), Maximum Consecutive Dry Day index (CDD), fraction of precipitation above the 95th intensity percentile (R95) and Hydroclimatic Intensity index (HY-INT). Comparison with coarse (GPCP) and high (TRMM) resolution daily precipitation data for the present day conditions shows that the precipitation intensity distributions from the GCMs are close to the GPCP data, while the RegCM4 ones are closer to TRMM, illustrating the added value of the increased resolution of the regional model. All global and regional model simulations project predominant increases in HWD, CDD, R95 and HY-INT, implying a regime shift towards more intense, less frequent rain events and increasing risk of heat wave, drought and flood with global warming. However, the magnitudes of the changes are generally larger in the global than the regional models, likely because of the relatively low “climate sensitivity” of the RegCM4, especially when using the CLM land surface scheme. In addition, pronounced regional differences in the change signals are found. The data from these simulations are available for use in impact assessment studies.


Heat Wave Global Climate Model Extreme Index Impact Assessment Study Global Climate Model Simulation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



This work was partially supported by grants from the project NextDATA funded by the Italian Consiglio Nazionale della Ricerca (CNR).

Supplementary material

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Copyright information

© Springer Science+Business Media Dordrecht 2014

Authors and Affiliations

  • Filippo Giorgi
    • 1
  • Erika Coppola
    • 1
  • Francesca Raffaele
    • 1
  • Gulilat Tefera Diro
    • 1
  • Ramon Fuentes-Franco
    • 2
  • Graziano Giuliani
    • 1
  • Ashu Mamgain
    • 3
  • Marta Pereira Llopart
    • 4
  • Laura Mariotti
    • 1
  • Csaba Torma
    • 1
  1. 1.Abdus Salam International Centre for Theoretical PhysicsTriesteItaly
  2. 2.Center for Scientific Research and Higher Education (CICESE)EnsenadaMexico
  3. 3.Centre for Atmospheric SciencesIndian Institute of Technology DelhiNew DelhiIndia
  4. 4.Department of Atmospheric SciencesUniversity of São PauloSão PauloBrazil

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