The climate modelling community has trialled a large number of metrics for evaluating the temporal performance of general circulation models (GCMs), while very little attention has been given to the assessment of their spatial performance, which is equally important. This study evaluated 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 minimum temperature over Pakistan using state-of-the-art spatial metrics, SPAtial EFficiency, fractions skill score, Goodman–Kruskal's lambda, Cramer's V, Mapcurves, and Kling–Gupta efficiency, for the period 1961–2005. The multi-model ensemble (MME) precipitation and maximum and minimum temperature data were generated through the intelligent merging of simulated precipitation and maximum and minimum temperature of selected GCMs employing random forest (RF) regression and simple mean (SM) techniques. The results indicated some differences in the ranks of GCMs for different spatial metrics. The overall ranks indicated NorESM1-M, MIROC5, BCC-CSM1-1, and ACCESS1-3 as the best GCMs in simulating the spatial patterns of mean annual, monsoon, winter, pre-monsoon, and post-monsoon precipitation and maximum and minimum temperature over Pakistan. MME precipitation and maximum and minimum temperature generated based on the best-performing GCMs showed more similarities with observed precipitation and maximum and minimum temperature compared to precipitation and maximum and minimum temperature simulated by individual GCMs. The MMEs developed using RF displayed better performance than the MMEs based on SM. Multiple spatial metrics have been used for the first time for selecting GCMs based on their capability to mimic the spatial patterns of annual and seasonal precipitation and maximum and minimum temperature. The approach proposed in the present study can be extended to any number of GCMs and climate variables and applicable to any region for the suitable selection of an ensemble of GCMs to reduce uncertainties in climate projections.

This study investigated the impacts of climate change on the urban heat island (UHI) and the number of very hot (maximum temperature >35°C) and very cold days (minimum temperature <5°C) in the central business district (CBD) of Melbourne city in Australia. A station located in Laverton (less urbanised area), which is 17 km southwest of Melbourne CBD, was selected as the reference station for the computation of UHI intensity in Melbourne CBD. Using daily minimum/maximum temperatures at the two stations, nocturnal/diurnal UHI intensities in Melbourne CBD were computed for the period 1952–2010. It was found that in Melbourne CBD, nocturnal UHI intensities show a clear rising trend over the period 1952–2010 unlike the diurnal UHI intensities. For the analysis of nocturnal UHI intensities in Melbourne CBD, under changing climate, for each calendar month statistical models based on the gene expression programming (GEP) technique were developed for downscaling general‐circulation model (GCM) outputs to monthly average minimum temperature at Melbourne CBD and Laverton. Using the outputs of HadCM3, GFDL2.0 and ECHAM5 pertaining to the A2 greenhouse gas emission scenario on the downscaling models, projections of monthly average minimum temperature were produced for the two stations over the period 2000–2099. In each season, at both stations, the ensemble average of monthly minimum temperature gradually increased over the period 2000–2099. The ensemble‐average UHI intensity in Melbourne CBD projected into the future was higher for all seasons in comparison to that of period 1952–1971. Downscaling models based on the GEP technique were developed for each calendar month for projecting the number of very hot days in November–March and very cold days in May–September in Melbourne CBD. It was found that, in the future, summer weather will spread to early autumn, and winter weather will move to early spring, in Melbourne CBD.

This study employed least square support vector machine regression (LS‐SVM‐R) and multi‐linear regression (MLR) for statistically downscaling monthly general circulation model (GCM) outputs directly to monthly catchment streamflows. The scope of the study was limited to calibration and validation of the downscaling models. The methodology was demonstrated by its application to a streamflow site in the Grampian water supply system in northwestern Victoria, Australia. Probable predictors for the study were selected from the National Center for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) reanalysis data set based on the past literature and hydrology. Probable variables that displayed the best significant correlations, consistently with the streamflows over the entire period of the study (1950–2010) and under three 20‐year time slices (1950–1969, 1970–1989 and 1990–2010) were selected as potential predictors. To better capture seasonal variations of streamflows, downscaling models were developed for each calendar month. The standardized potential predictors were introduced to the LS‐SVM‐R and MLR models, starting from the best correlated three and then, others one by one, based on their correlations with the streamflows, until the model performance in validation was maximized. This stepwise model development enabled the identification of the optimum number of potential variables for each month. The model calibration was performed over the period 1950–1989 and validation was done for 1990–2010. LS‐SVM‐R model parameter optimization was achieved using simplex algorithm and leave‐one‐out cross‐validation. The MLR models were optimized by minimizing the sum of squared errors. In both modelling techniques, validation was performed as an independent simulation. In calibration, LS‐SVM‐R and MLR models displayed equally good performances with a trend of under‐predicting high flows. During validation, LS‐SVM‐R outperformed MLR, though both techniques over‐predicted most of the streamflows. It was concluded that LS‐SVM‐R is a better technique for statistically downscaling GCM outputs to streamflows than MLR, but still MLR is a potential technique for the same task. Copyright © 2012 Royal Meteorological Society