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. The climate modelling community has trialled a largenumber of metrics for evaluating the temporal performance of general circulationmodels (GCMs), while very little attention has been given to the assessmentof their spatial performance, which is equally important. This studyevaluated the performance of 36 Coupled Model Intercomparison Project 5(CMIP5) GCMs in relation to their skills in simulating mean annual, monsoon,winter, pre-monsoon, and post-monsoon precipitation and maximum and minimumtemperature over Pakistan using state-of-the-art spatial metrics, SPAtialEFficiency, fractions skill score, Goodman–Kruskal's lambda, Cramer's V,Mapcurves, and Kling–Gupta efficiency, for the period 1961–2005. Themulti-model ensemble (MME) precipitation and maximum and minimum temperaturedata were generated through the intelligent merging of simulatedprecipitation and maximum and minimum temperature of selected GCMs employingrandom forest (RF) regression and simple mean (SM) techniques. The results indicatedsome differences in the ranks of GCMs for different spatial metrics. Theoverall ranks indicated NorESM1-M, MIROC5, BCC-CSM1-1, and ACCESS1-3 as thebest GCMs in simulating the spatial patterns of mean annual, monsoon,winter, pre-monsoon, and post-monsoon precipitation and maximum and minimumtemperature over Pakistan. MME precipitation and maximum and minimumtemperature generated based on the best-performing GCMs showed moresimilarities with observed precipitation and maximum and minimum temperaturecompared to precipitation and maximum and minimum temperature simulated byindividual GCMs. The MMEs developed using RF displayed better performancethan the MMEs based on SM. Multiple spatial metrics have been used for thefirst time for selecting GCMs based on their capability to mimic the spatialpatterns of annual and seasonal precipitation and maximum and minimumtemperature. The approach proposed in the present study can be extended toany number of GCMs and climate variables and applicable to any region forthe suitable selection of an ensemble of GCMs to reduce uncertainties inclimate projections.

hydrology and earth system sciences

Selection of multi-model ensemble of general circulation models for the simulation of precipitation and maximum and minimum temperature based on spatial assessment metrics