Climate change projections from Coupled Model Intercomparison Project phase 5 multi-model weighted ensembles for Mexico, the North American monsoon, and the mid-summer drought region

Two versions of the reliability ensemble averaging (REA and REA Xu) method and an unweighted mean were used to generate multi-model ensembles of 14 general circulation models (GCMs) from the Coupled Model Intercomparison Project Phase 5 (CMIP5) for a baseline (1971-2000) period and future scenarios (RCP4.5 and RCP8.5) for the 21st century. To test the consistency of the REA ensembles at different scales, one ensemble was area-averaged at regional scale and two were obtained at 50-km gridpoints. Climatic metrics of temperature and precipitation during the baseline period served to evaluate the performance of the GCMs and the ensembles in the North American monsoon (NAM) and the mid-summer drought (MSD) regions. The metrics of the three REA ensembles are very similar among them in each region and show a better performance than the unweighted multi-model ensemble (U-MME). The majority of the GCMs overestimate winter precipitation in the NAM and fail to retract the end of the monsoon in autumn; REA ensembles reduce this overestimation. All ensembles capture well the MSD's double peak of rainfall, but underestimate summer precipitation. According to all ensembles, temperature increases of 1.5-2 degrees C in the two regions may be reached between 2035 and 2055 relative to the baseline, and by 2070-2099 temperature (precipitation) in Mexico may increase (decrease) between 2 degrees C (5%) and 5.8 degrees C (10%) in the RCP4.5 and RCP8.5 scenarios, respectively. Annual changes of precipitation show a north (positive) and south of 35 degrees N (negative) pattern. The largest impacts are expected during summer with a possible decrease of similar to 13% (up to -1.5 mm/day), especially in southern Mexico, Central America, and the Caribbean, while autumn precipitation may slightly increase. Future projections from the REA Xu (by gridpoint) ensemble show spatial patterns similar to the U-MME, but with more regional detail, which could be an added value for regional climate impact studies.