Performance evaluation of six RCMs for precipitation and temperature in a semi-arid region

Global Circulation Models (GCMs) and Regional Climate Models (RCMs) are fundamental tools for investigating climate change and its impact. This study focuses on evaluation the ability of two Regional Climate Models (RCMs), REMO2015 and RegCM4-7, to simulate minimum and maximum temperature as well as monthly precipitation in Northwestern Iraq. The evaluation is based on comparisons with observed data from four key stations in the region. Both the regional models, REMO2015 and RegCM4-7, are driven by three GCMs, so the number of models evaluated is six GCM-RCM combination models. The performance of the six GCM-RCM model outputs was evaluated against observed data using a suite of statistical criteria: Bias Percent (PBIAS), Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Correlation Coefficient (R), and Cumulative Distribution Function (CDF). Additionally, the Innovative Test (IT) was employed to analyze trends in both annual and monthly precipitation data. Also, Bias correction was employed the “Linear scaling: method to correct temperature and “Distribution Mapping” method for monthly precipitation. In general, the six models exhibited better performance in simulating minimum and maximum temperature than monthly precipitation. An interesting finding is that all six models exhibited similar behavior in simulating maximum temperature across all stations. Statistical criteria values revealed that the NCC-RegCM4-7 model produces the best results for minimum temperature simulation. Meanwhile for in maximum temperature, both MOHC-REMO2015 and NCC-REMO2015 emerged as the top performers. Additionally, MIROC-RegCM4-7 demonstrated the greatest skill in capturing t trends in the observed data, closely matching the trends measured at the studied stations. The application of the linear scaling method effectively addressed the biases present in the raw temperature data from the six GCM-RCM combinations. This correlation resulted in reduced MAE and RMSE values for both minimum and maximum temperatures. Similarly, the DM method successfully removed biases in the monthly precipitation simulation from all six models.