Evaluating <scp>CMIP6</scp> Global Climate Models Performances Over Nigeria: An Integrated Approach

ABSTRACT The choice of global climate models (GCMs) for climate or hydrological studies remains a challenge due to their temporal and spatial variations and different performances in different parts of the globe. This study assesses the performances of 33 GCMs of the Coupled Model Intercomparison Project Phase 6 (CMIP6) for precipitation, maximum temperature and minimum temperature over Nigeria in order to select the best performing GCMs for aggregation into a multi model ensemble (MME). The study uses three statistical metrics (SM) and Random Forest (RF) machine learning method for the evaluation of the GCMs. In addition, the GCM performances were also estimated using spatial assessment, boxplots, scatter plots and mean monthly comparison at each grid point over the period 1985–2014. Finally, the average was used to generate variations of MMEs by increasing the number of models in the MME considering the inclusion of the better ranking ones first in order to determine the optimum MME for the variables. The highest ranking GCMs based on the average of the scores of the SM and RF were NESM3, CMCC‐ES, IPSL‐L‐R‐INCA and IPSL‐L‐R, MPI‐HAM and SAMO‐UNICON for precipitation; BCC‐C‐MR, MRI‐ESM, BCC‐ESM1, ACC‐ESM1‐5 and GISS‐E2‐CC for maximum temperature; and GFDL‐ESM, AWI‐C‐MR, IPSL‐L‐R, CAS‐ESM2 and AWI‐C‐LR for minimum temperature. The highest‐ranking model for all variables is ACC‐ESM1‐5, which ranked highest with a score of 0.6920 followed by BCC‐C‐MR with 0.6898, CAS‐ESM2 with 0.6597 and BCC‐ESM1 with 0.6545 score. The results of the spatial assessment, boxplots, scatter plots and the mean monthly comparison aligns with this. In the aggregation of MME for the three variables, the optimum number of models was obtained after averaging of the first four best ranking GCMs. This study presents a localised study, which is expected to reduce uncertainty in the projection of climate over Nigeria.