Performance evaluation of CMIP6 HighResMIP models in simulating precipitation over Madagascar

Herijaona Hani Roge Hundilida Randriatsara, Zhenghua Hu, Xiyan Xu, Brian Ayugi, Kenny Thiam Choy Lim Kam Sian, Richard Mumo, Victor Ongoma, Eva Holtanova

Research output: Contribution to journalArticlepeer-review


The present study evaluates the performance of high-resolution global climate models derived from Coupled Model Intercomparison Project Phase 6 (CMIP6 HighResMIP), in simulating rainfall characteristics over Madagascar on an annual and seasonal scales for the period 1981–2014. The models and their ensemble mean are assessed based on two observational datasets sourced from Climate Hazards Group Infrared Precipitation with Station data version 2 (CHIRPS v2.0) data and the European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis fifth generation-Land dataset (ERA5) as the references throughout the diverse analyses. A Taylor diagram, accompanied by the Taylor skill score (TSS), is used for the annual and seasonal model-rankings and the overall performance of the models. The best-performing models are EC-Earth3P-HR, ECMWF-IFS-HR, ECMWF-IFS-LR and HadGEM3-GC31-MM. The least-recommended models with remarkable biases are BCC-CSM2-HR, CAMS-CSM1-0, FGOALS-f3-H, MPI-ESM1-2-HR and MPI-ESM1-2-XR. It is worth mentioning that FGOALS-f3-H tends to overestimate rainfall in most analyses, while MPI-ESM1-2-HR and MPI-ESM1-2-XR underestimate it. The findings of this study are of great importance to climatologists and present an opportunity for further investigation of underlying processes responsible for the observed wet/dry biases in order to improve the forecast skills in the models over the study area.

Original languageEnglish
Pages (from-to)5401-5421
Number of pages21
JournalInternational Journal of Climatology
Issue number12
Publication statusAccepted/In press - 2023

All Science Journal Classification (ASJC) codes

  • Atmospheric Science


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