Comparison of GCM precipitation predictions with their RMSEs and pattern correlation coefficients

Chulsang Yoo, Eunsaem Cho

Research output: Contribution to journalArticlepeer-review

15 Citations (Scopus)


This study evaluated 20 general circulation models (GCMs) of the Coupled Model Intercomparison Project, Phase 5 (CMIP5), which provide the prediction results for the period of 2006 to 2014, the period from which the observation data (the Global Precipitation Climatology Project (GPCP) data) are available. Both the GCM predictions of precipitation and the GPCP data were compared for three data structures-the global, zonal, and grid mean-with conventional statistics like the root mean square error (RMSE) and the pattern correlation coefficient of the cyclostationary empirical orthogonal functions (CSEOFs). As a result, it was possible to select a GCM which showed the best performance among the 20 GCMs considered in this study. Overall, the NorSM1-M model was found to be the most similar to the GPCP data. Additionally, the IPSL-CM5A-LR, BCC-CSM, and GFDL-CMS models were also found to be quite similar to the GPCP data.

Original languageEnglish
Article number28
JournalWater (Switzerland)
Issue number1
Publication statusPublished - 2018 Jan 2


  • GCM precipitation
  • Pattern correlation
  • Performance evaluation

ASJC Scopus subject areas

  • Biochemistry
  • Geography, Planning and Development
  • Aquatic Science
  • Water Science and Technology


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