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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]

Quantifying and separating different sources of uncertainty helps to improve the understanding of the projected effects of climate change and can inform decision‐making in adaptation planning. This paper (1) evaluated four process‐based crop models; (2) assessed the effects of climate change on maize yield using climate change outputs from seven global climate models (GCMs) under three representative concentration pathways (RCPs); and (3) disaggregated the contributions of multiple crop models, GCMs and RCPs to overall uncertainty. All four models captured more than 80% of the variation in days to silking, maturity and yield, indicating reasonably reproduced observations. Similarly, the root mean square errors were moderate for days to silking and maturity (fewer than 4 days) and yield (0.5–0.7 t/ha). Overall, the results indicate that the models could assess grain yield at the study sites reasonably well. The results of the multiple models ensemble indicate that the maize yield will decrease by 9–11% with a probability of 72–80% on average during the period 2010–2039 relative to the baseline (1976–2005). The uncertainty in the maize‐yield simulations might arise mostly from the GCM models, followed by the crop models and RCPs, the contribution of which could be neglected relative to the other factors. Therefore, the use of a multiple crop model and a GCM ensemble is advisable in order to account properly for uncertainties in crop assessments. The uncertainty in maize‐yield simulations might arise mostly from global climate models (GCMs), followed by crop models and representative concentration pathways (RCPs), the contribution of which could be neglected relative to the other factors. Therefore, the use of a multiple crop model and a GCM ensemble is advisable in order to account properly for uncertainties in crop assessments. [ABSTRACT FROM AUTHOR]

Although the Urmia Lake has undergone remarkable drought conditions within the past decades mainly due to climate change, drought studies covering the entire Urmia Lake Basin and all drought aspects are lacking. The present study investigated the spatial and temporal drought conditions in the Urmia Lake Basin for the past (1988-2017) and future (2021-2050 and 2051-2080) periods using five general circulation models (GCMs) under the IPCC (Intergovernmental Panel on Climate Change) scenarios RCP 2.6, RCP 4.5, and RCP 8.5. The standardized precipitation index (SPI) and standardized precipitation and evapotranspiration index (SPEI) were compared. The SPEI predicted more drought events than the SPI, and it seemed to be a more suitable drought index than the SPI for the basin. In general, the future periods would encounter less drought conditions in terms of significant drought trends and duration than the observed period under all scenarios, but the frequency of quarterly severe drought events in the future periods would be higher than in the observed period. Furthermore, the stations Urmia (western bank) and Tabriz and Maragheh (eastern banks) would face the highest frequency of different types of quarterly drought events in the future periods compared with the observed period. The predicted high frequencies of drought events for the future periods can intensify the current low water level situation of the Urmia Lake, which seriously threatens all types of ecosystems in the basin. Therefore, serious actions need to be taken into account for efficient ecosystem and water resources management in the basin. [ABSTRACT FROM AUTHOR]

This exploratory research examines the impacts of changing climate on the vulnerable US‐Affiliated Pacific Islands (USAPI) from the perspective of the Intergovernmental Panel on Climate Change's (IPCC) Fifth Assessment Report (AR5) coupled with General Circulation Models (GCMs). Island‐wide projections of future climate change (e.g. temperature, rainfall, and net water flux) were made using the latest IPCC AR5 GCMs protocol (Coupled Model Intercomparison Project Phase—CMIP5) with 38 GCMs with up to 105 model runs. A review was also made of studies on model‐based future projections of the El Niño–Southern Oscillation (ENSO). The CMIP5 model's results clearly illustrate that the past trend in temperature (1950–2017) is rising while the rainfall trend remains more or less static. It is also clear from the projections that the long‐term trend for temperature rise is fast and significant, while the trend for rainfall and net water flux (P‐E) rise appears to be slow and marginal. On the perspective of CMIP5 model's evaluation for the USAPI region, the temperature projections are found to be promising, while the rainfall projection potentials, despite some limitations, are also encouraging. The prime concerns for future disruptions in the USAPI region are the consequences of increasing frequency of the ENSO and related rainfall activities. The long‐term warming signal may further complicate the problem. Therefore, the currently water‐stressed islands and low‐lying atolls in the Federated States of Micronesia (FSM) and Republic of Marshalls Islands (RMI) are particularly vulnerable to El Niño‐related heat stress or drought and La Niña‐related inundations or flooding. In both cases, the future demand‐oriented climate‐sensitive water resources sector will be severely affected. A climate‐information‐based comprehensive water resources management plan (for the 2030s) is therefore essential with more detailed ENSO‐related climate information and impacts in terms people can understand and respond to. [ABSTRACT FROM AUTHOR]