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This paper evaluated and compared the performance of a statistical downscaling method and a dynamical downscaling method to simulate the spatial–temporal rainfall distribution. Outputs from RegCM4 Regional Climate Model (RCM) and the CanESM2 Atmosphere–Ocean General Circulation Model (AOGCM) were selected for the data scarce Gwadar‐Ormara basin, Pakistan. The evaluation was based on the climatological average and standard deviation for historic (1971–2000) and future (2041–2070) time periods under Representative Concentration Pathways (RCP) 4.5 and 8.5 scenarios. The performance evaluation showed that statistical downscaling is preferred to simulate and project rainfall patterns in the study area. Additionally, the Statistical DownScaling Model (SDSM) showed low R2 values in calibration and validation of the simulations with respect to observed data for the historic period. Overall, SDSM generated satisfactory results in simulating the monthly rainfall cycle of the entire basin. In this study, RegCM4 showed large rainfall errors and missed one rainfall season in the historic period. This study also explored whether the grid‐based rainfall time series of the Asian Precipitation—Highly Resolved Observational Daily Integration Towards Evaluation (APHRODITE) dataset could be used to enlarge and complement the sample of in situ observed rainfall time series. A spatial correlogram was used for observed and APHRODITE rainfall data to assess the consistency between the two data sources, which resulted in rejecting APHRODITE data. For the future time period (2041–2070) under RCPs 4.5 and 8.5 scenarios, rainfall projections did not show significant difference for both downscaling approaches. This may relate to the driving model (CanESM2 AOGCM) and not necessarily suggests poor performance of downscaling; either statistical or dynamical. Hence, the study recommends evaluating a multi‐model ensemble including other GCMs and RCMs for the same area of study. [ABSTRACT FROM AUTHOR]

ABSTRACT: One of the most important stages in climate‐change‐impact studies is uncertainty analysis, due to its great effect on both predictions and decision‐making. This study presents a procedure that characterizes the changes of climatic variables for the period 2011–2040 under representative concentration pathway (RCP) scenarios and then quantifies the uncertainty linked with the downscaling process using a bootstrapping method at 95% confidence intervals in one of the most vulnerable basins, the “Karaj‐Jajrud” located in the South Alborz Range, Iran. The results show that there is a consistent warming in mean air temperature time‐series with different magnitudes for all the RCP scenarios in the region for 2011–2040, whereas the results indicate decreasing precipitation compared with the baseline period for all RCP scenarios in the study area. Analysing the impacts of the downscaling process uncertainty on the prediction results shows that the contribution of this uncertainty source to the prediction uncertainty is relatively high, as about 30% of the downscaled temperature and precipitation data fall inside the 95% simulation confidence intervals. Furthermore, precipitation‐series uncertainty is more than the air temperature series. Climate change assessments and their uncertainty analysis can help managers to enhance preparedness and adaptation strategies in order to mitigate the consequences of natural hazards. More investigations can be done by adopting more general circulation models and other downscaling methods to compare the uncertainty that arises from each uncertainty source. [ABSTRACT FROM AUTHOR]