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General circulation models (GCMs), used for climate change projections, should be able to simulate both the temporal variability and spatial patterns of the observed climate. However, the selection of GCMs in most previous studies was either based on temporal variability or mean spatial pattern of past climate. In this study, a framework is proposed for the selection of GCMs based on their ability to reproduce the spatial patterns for different climate variables. The Kling‐Gupta efficiency (KGE) was used to assess GCMs ability to simulate the annual spatial patterns of maximum and minimum temperatures (Tmx and Tmn, respectively) and rainfall depth. The mean and standard deviation of KGEs were used as performance indicators to present the GCMs' overall skill. Finally, the global performance indicator was used as a multi‐criteria decision‐making approach to integrate the results of different climate variables and seasons in order to rank the GCMs. Egypt was considered as a case study. The results revealed the better performance, in order, of the MRI‐CGCM3, followed by FGOALS‐g2, GFDL‐ESM2G, GFDL‐CM3 and lastly MPI‐ESM‐MR over Egypt. The final set of GCMs showed a similar spatial pattern for the projected change in temperature over Egypt. For different scenarios, Tmx was projected to increase in the range of 1.63–4.2°C while the increase in Tmn ranged between 1.28 and 4.43°C. A projected increase in temperature in winter is likely greater than in summer. The selected models also projected a 62% decrease in rainfall depth over the northern coastline where rain is currently most abundant while an increase in the dry southern zones. The rise in temperature and decrease in rainfall depth could have severe implications for a country with dwindling water resources.

Selection of appropriate empirical reference evapotranspiration (ETo) estimation models is very important for the management of agriculture, water resources, and environment. Statistical metrics generally used for performance assessment of empirical ETo models, on a station level, often give contradictory results, which make the ranking of methods a challenging task. Besides, the ranking of ETo estimation methods for a given study area based on the rank at different stations is also a difficult task. Compromise programming and group decision-making methods have been proposed in this study for the ranking of 31 empirical ETo models for Peninsular Malaysia based on four standard statistical metrics. The result revealed the Penman-Monteith as the most suitable method of estimation of ETo, followed by radiation-based Priestley and Taylor and the mass transfer-based Dalton and Meyer methods. Among the temperature-based methods, Ivanov was found the best. The methodology suggested in this study can be adopted in any other region for an easy but robust evaluation of empirical ETo models.

The performance of three satellite-based high-resolution gridded rainfall datasets, namely the gauge corrected Global Satellite Mapping of Precipitation (GSMaP), Integrated Multi-Satellite Retrievals for Global Precipitation Measurement (IMERG), and the Climate Hazards Group InfraRed Precipitation with Stations (CHIRPS) in the hot desert climate of Egypt were assessed. Seven statistical indices including four categorical indices were used to assess the capability of the products in estimating the daily rainfall amounts and detecting the occurrences of rainfall under different intensity classes from March 2014 to May 2018. Although the products were gauge-corrected, none of them showed a consistent performance, and thus could not be titled as the best or worst performing product over Egypt. The CHIRPS was found to be the best product in estimating rainfall amounts when all rainfall events were considered and IMERG was found as the worst. However, IMERG was better at detecting the occurrence of rainfall than CHIRPS. For heavy rainfall events, IMERG was better at the majority of the stations in terms of the Kling–Gupta efficiency index (−0.34) and skill-score (0.33). The IMERG was able to show the spatial variability of rainfall during the recent big flash flood event that hit Northern Egypt. The study indicates that accurate estimation of rainfall in the hot desert climate using satellite sensors remains a challenge.

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