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Climatic change over the globe due to global warming affects the characteristics of climate variables that have critical implications on large fraction of population that depends on agriculture for livelihood like Pakistan. Consequently, this study examined how high horizontal grid resolution CMIP6 models simulate the observed precipitation variability during 1981–2014 and further explored the future changes during 2017–2050 under high emission scenario SSP5‐8.5 over Pakistan region. The performances of 12 (CMIP6) High Resolution Model Inter‐comparison Project version 1.0 (hereafter; HighResMIP) GCMs and their ensemble means in reproducing the observed climate were calculated at each station in the study domain and formed the basis for deriving HighResMIP ranking. Further, the study employed Shannon's Entropy and a modified version of Criteria Importance through Inter‐Criteria Correlation (D‐CRITIC) method to build an ensemble mean from the best performing models. Evaluation of HighResMIP GCMs performance revealed that most models showed mixed signals in the region, with fewer models such as HadGEM3‐GC31‐HH, HadGEM3‐GC31‐HM and HadGEM3‐GC31‐MM showing good agreement with the observed precipitation. Overall, HighResMIP multi‐model ensemble outperforms precipitation distribution over individual models. D‐CRITIC based ensemble mean implies higher increase in precipitation than entropy approach. Future changes depict an increase in mean annual in the northern region relative to the historical period. A pronounced increase of about 16%–18% in precipitation was noted in HadGEM3‐GC31‐HH and HiRAM‐SIT‐HR. Conversely, FGOAL‐f3‐H project noteworthy reduction (21%) in precipitation in the near future (2017–2050). The projected seasonal precipitation shows upsurge pattern of 5%–28% in pre‐monsoon season, whereas the reduction in monsoon precipitation is projected to be 29%–40%. The findings of this study can help in building future climate resilience and developing strategic policies in Pakistan. [ABSTRACT FROM AUTHOR]

This study estimated global climate change signals at different latitudes for four main Shared Socioeconomic Pathways (SSPs). Five evaluation metrics were integrated using the Technique for Order of Preference by Similarity to Ideal Solution to quantify the historical reproducibility of 25 CMIP6 General Circulation Models (GCMs) with Global Precipitation Climatology Centre precipitation and Climatic Research Unit temperature as the reference. The most suitable GCMs for simulating climate over different latitudes, selected based on evaluation metrics, were used to prepare a multimodel ensemble and project the future annual and seasonal precipitation and temperature in the near (2031–2065) and far future (2066–2100). The results showed that GCMs estimated the historical mean temperature efficiently but underestimated the monthly precipitation compared to the reference data. The changes in precipitation and temperature at mid‐latitudes (N45.5°–60°) showed the highest variability for all scenarios. The maximum increases in both climate variables for SSP5‐8.5 were 80.5% and 4.8% at N45.5°–60°, respectively. In contrast, the temperature and precipitation at S30.5°–45° revealed a decreasing pattern. Mid‐latitude winter (S30.5°–45°) would be drier in the future than in the base period (1980–2014). This study showed that precipitation variability and the mean temperature in the northern hemisphere would be larger for SSPs with higher radiative forcing. Therefore, the results of this study help improve knowledge of global future climate change by latitudes. Plain Language Summary: Climate change is already contributing to various unpredictable phenomena in many fields. A well‐known organization that periodically evaluates the impacts of climate change and actionable response strategies, the IPCC assessment report states that climate change is already directly impacting ecosystems, water cycles, and human activities. Therefore, sufficient exploration of the future climate change is vital for systematically developing a plan for climate change mitigation and adaptation, and the Shared Socioeconomic Pathway scenario contains various factors such as social, economic, and physics, making it reasonable for projecting the future climate. This study evaluated the historical monthly temperature and precipitation reproducibility of CMIP6 General Circulation Model (GCM) using various metrics. Based on this, multi‐model ensemble was built using Technique for Order of Preference by Similarity to Ideal Solution, a multi‐criteria decision‐making technique, for a reasonable future climate assessment. The results of this study showed that the monthly precipitation of CMIP6 GCM over the historical period is overestimated than the reference data, but the monthly temperature performance is stable. For projected future climate, high latitudes in the northern hemisphere are most vulnerable to changes in temperature and precipitation, and the southern hemisphere captured robust dryness for the future. Key Points: General Circulation Models' performances are different by each latitude and their simulations were overestimated for rainfall and well‐estimated for temperatureThe region in N75‐N90 would be most vulnerable to climate change in the future, and the area in S30‐S60 would be drier in the futureVariability of the northern hemisphere would increase more for high emission scenarios but seasonal trends are more chaotic than in the past [ABSTRACT FROM AUTHOR]

Changes in precipitation and temperature have crucial implications in the arid region due to their fragile environment. This study was an attempt to estimate possible spatiotemporal alteration of annual and seasonal precipitation and temperature in Iraq. Statistical downscaling of Coupled Model Intercomparison Project Phase 6 (CMIP6) global climate model (GCM) simulations for different shared socioeconomic pathways (SSP) was utilized. The GCMs were ranked according to their skills in simulating climate research unit (CRU) precipitation and temperature climatology along with their seasonality. Nonlocal model output statistics (MOS) models were implemented using a support vector machine (SVM) for downscaling and projection of selected GCM precipitation and temperature. Results revealed ACCESS‐CM2, BCC‐CSM2‐MR, GISS‐E2‐1‐G and MRI‐ESM2‐0 GCMs are most suitable for Iraq. The spatiotemporal changes in precipitation indicated a substantial decrease to north (up to −7.8%·mm−1) while an increase (around 3.0%) to south for different SSPs. Far future (2060–2099) showed both increase and decrease in precipitation than near future (2020–2059). The precipitation was projected to reduce in winter and increase in summer for all climate zones during both periods. The maximum temperature was projected to increase by 4.5°C to the north and 0.9–2°C to the south. In contrast, the minimum temperature was projected to rise by 1.0–3.5°C to both north and south. Both maximum and minimum temperatures may increase; however, more increases might be in winter and less in summer. The minimum temperature increase will be higher than the maximum temperature in the cold northern region and vice versa. Uncertainty in precipitation and temperature projections was higher for the far‐future period with higher SSPs than for the near‐future period with lower SSPs. The results of this study can guide the development of strategic policies for climate resiliency development in Iraq. [ABSTRACT FROM AUTHOR]

This study quantified the uncertainties of future drought projections in the South Korea case by means of the reliability ensemble averaging (REA) of 10 GCM equivalents of the Coupled Model Intercomparison Project, Phases 5 and 6 (CMIP5 and CMIP6). Two meteorological drought indices, the Standardized Precipitation Index (SPI) and the Standardized Precipitation‐Evapotranspiration Index (SPEI) for Representative Concentration Pathway (RCP) 4.5 and RCP8.5 of CMIP5 and Shared Socioeconomic Pathway (SSP) 2‐4.5 and SSP5‐8.5 of CMIP6 were considered. The GCMs' performances for the historical period were evaluated, and their biases were corrected using quantile mapping. The multi‐model ensemble (MME) means of the GCMs were generated using the entropy and TOPSIS methods. The results showed that the TOPSIS MME estimated more intense droughts than the entropy MME. The levels of SPI and SPEI severity estimated using the entropy MME were higher than those from the TOPSIS MME. The SPI and SPEI outcomes of RCP4.5 were much more robust than those of SSP2‐4.5. The projected drought severity in the near future was much greater than in the far future, while the reliability of drought projections in the far future was much higher than in the near future. The reliability levels of drought projections for the SSP scenarios were higher than for the RCPs for most durations. This study can support planning and management of future droughts considering uncertainty variables. [ABSTRACT FROM AUTHOR]

This study compared the historical simulations and future projections of precipitation and temperature of Coupled Model Intercomparison Project (CMIP)5 and CMIP6 general circulation models (GCMs) to quantify the differences in the projections due to differences in scenarios. Five performance indicators were used to quantify the model reproducibility of the observed precipitation levels at 22 stations for the historical period of 1970–2005. The percentages of change in precipitation and temperature were estimated for the near (2025–2060) and far future (2065–2100) for two Representative Concentration Pathway (RCP)4.5 and RCP8.5 scenarios of CMIP5 and two Shared Socioeconomic Pathway (SSP)2–4.5 and SSP5‐8.5 scenarios of CMIP6. The uncertainty in the projection in each case was calculated using the reliability ensemble average (REA) method. As a result, the CMIP6 GCMs showed an improvement compared with the CMIP5 GCMs with regard to the ability to simulate the historical climate. The uncertainty in the precipitation projections was higher for SSPs than that in RCPs. With regard to the temperature, the uncertainty was higher for RCPs than for SSPs. The ensemble means of the precipitation and temperature showed higher changes in the far future compared with the near future for both RCPs and SSPs. This study contributes to improvement in the confidence of future projections using CMIP6 GCMs and bolsters our understanding of the relative uncertainty in SSPs and RCPs. [ABSTRACT FROM AUTHOR]

Stream water‐use is essential for both agricultural and hydrological management and yet not many studies have explored its non‐stationarity and nonlinearity with meteorological variables. This study proposed a deep‐learning based model to estimate agricultural water withdrawal using hydro‐meteorological variables, which projected the changes of agricultural water withdrawal influenced by climate change of future. The relationships between meteorological variables and stream water‐use rate (WUR) were quantified using a deep belief network (DBN). The influences of precipitation, potential evapotranspiration, and monthly averaged WUR on the performance of the developed DBN model were tested. As a result, this DBN with potential evapotranspiration (PET) provided better performances than precipitation to estimate the WUR. The PET of multi‐model scenarios for Representative Concentration Pathways 8.5 would be increased as time goes by, and thus leads to increase WUR estimated by DBN in three basins, located in South Korea during the future period. On the contrary, water availability expected to decrease compared to the current. Therefore, managing water‐uses and improving efficiencies can be prepared for the change in agricultural water‐use by climate change in the future. [ABSTRACT FROM AUTHOR]

ABSTRACT The choice of global climate models (GCMs) for climate or hydrological studies remains a challenge due to their temporal and spatial variations and different performances in different parts of the globe. This study assesses the performances of 33 GCMs of the Coupled Model Intercomparison Project Phase 6 (CMIP6) for precipitation, maximum temperature and minimum temperature over Nigeria in order to select the best performing GCMs for aggregation into a multi model ensemble (MME). The study uses three statistical metrics (SM) and Random Forest (RF) machine learning method for the evaluation of the GCMs. In addition, the GCM performances were also estimated using spatial assessment, boxplots, scatter plots and mean monthly comparison at each grid point over the period 1985–2014. Finally, the average was used to generate variations of MMEs by increasing the number of models in the MME considering the inclusion of the better ranking ones first in order to determine the optimum MME for the variables. The highest ranking GCMs based on the average of the scores of the SM and RF were NESM3, CMCC‐ES, IPSL‐L‐R‐INCA and IPSL‐L‐R, MPI‐HAM and SAMO‐UNICON for precipitation; BCC‐C‐MR, MRI‐ESM, BCC‐ESM1, ACC‐ESM1‐5 and GISS‐E2‐CC for maximum temperature; and GFDL‐ESM, AWI‐C‐MR, IPSL‐L‐R, CAS‐ESM2 and AWI‐C‐LR for minimum temperature. The highest‐ranking model for all variables is ACC‐ESM1‐5, which ranked highest with a score of 0.6920 followed by BCC‐C‐MR with 0.6898, CAS‐ESM2 with 0.6597 and BCC‐ESM1 with 0.6545 score. The results of the spatial assessment, boxplots, scatter plots and the mean monthly comparison aligns with this. In the aggregation of MME for the three variables, the optimum number of models was obtained after averaging of the first four best ranking GCMs. This study presents a localised study, which is expected to reduce uncertainty in the projection of climate over Nigeria. [ABSTRACT FROM AUTHOR]

The presence of long‐term persistence (LTP) in hydroclimatic time series can lead to considerable change in the significance of trend. Therefore, past findings of climatic trend analysis without considering LTP in time series has become a disputable issue. The objective of this study was to assess the spatial patterns in the trends of annual and seasonal rainfall amounts and extremes in Peninsular Malaysia considering LTP. Daily rainfall data of APHRODITE (Asian Precipitation – Highly‐Resolved Observational Data Integration Towards Evaluation) for the period 1951–2007 was used to assess the trends using the classical Mann–Kendall (MK) test and the modified version of the MK test, which can remove the influence of LTP in the significance of trends. The results indicate that significant trends in different rainfall indices of Peninsular Malaysia obtained using the MK test reduced drastically when LTP was taken into consideration. There was almost no change in annual and seasonal rainfall amounts, which contradicts the findings of previous studies. The field significance of regional trends revealed an increase in wet spells at an average rate of 4.8 and 4.9 days/decade in the southeast and the southwest respectively during the northeast monsoon, and a decrease in rainy days by −1.4 days/decade in the north and an increase both in dry spells by 1.0 day/decade in the southeast and in the maximum 1 day rainfall by 1.7 mm/decade on the west coast during the southwest monsoon. The results indicate that the trends in rainfall indices reported in the maritime continent in previous studies should be re‐evaluated as most of them are due to LTP. [ABSTRACT FROM AUTHOR]

This study explores the application of a civic perception analysis in the context of stream restoration as a social‐ecological system (SES). Analysing such a SES is useful for understanding correlations between civic interests and environmental conditions in river restoration. An analytic hierarchy process (AHP) analysis was used to quantify the demand for restoration objectives. Additionally, an image‐based survey was used to precisely capture residents' perceptions of urban stream restoration by comparing aesthetic images, hydrologic functions, and environmental quality. Based on the AHP results, a hierarchical multiple regression was derived. A total of 181 participants responded to our survey, and the results were summarized by each subwatershed in the An'Yang watershed. The results demonstrated that the perceptions of residents who live in various environmental and regional conditions may manifoldly reflect their personal visual and aesthetic preferences by subwatershed. The upstream subwatershed values (SW1 = 7.86 and SW2 = 7.39) were higher than those in the downstream areas (SW5 = 5.28 and SW6 = 5.15). Consequently, this hierarchical multilevel model showed that individual environmental perception was highly associated with visual preference regarding stream restoration at the subwatershed level (p < 0.001). Urban environmental and spatial planners could use such information when restoring and designing waterfront spaces. [ABSTRACT FROM AUTHOR]

Abstract: This research explores if individual preferences for the major functions of stream restoration processes correlate water quality variations in an urban watershed of South Korea. We use a set of results from an analytical hierarchy process model to rank the major stream restoration functions and compare citizens' preferences for “water quality” improvement during stream restoration based on the water quality index (WQI) and ordinary least square regression. This study identifies the correlation between the WQI and individual perceptions regarding stream restoration. Regional context and individual characteristics also influence the demands and awareness of citizens pertaining to stream restoration. Our findings highlight the importance of understanding residents' perceptions and demographic information, including education, marriage status, gender, and residence period in different subwatersheds. The largest change (28.29) in the WQI score of one subwatershed was correlated with the participants' perception of water quality changes and water pollution (analytical hierarchy process index: 66.8%). On the basis of the survey results, we recommend that a human–ecological interdependent and adaptive consultative body is established in each subwatershed to successfully implement stream restoration projects. [ABSTRACT FROM AUTHOR]

This study proposes a robust prioritization framework for climate change adaptation strategies under uncertain climate change scenarios, using the VIseKriterijumska Optimizacija I Kompromisno Resenje ( VIKOR) method, a multi-criteria decision-making approach, together with the Shannon entropy-based weights. The VIKOR method allows us to find a compromise solution between two decision strategies of maximizing group utility and minimizing individual regret, and the Shannon entropy is used to determine objective weights among multiple climate change scenarios. The proposed methodology was applied to the problem of selecting locations of subwatersheds for reusing treated wastewater ( TWW) in a Korean urban watershed. Selected based on the sustainability concept, hydro-environmental and socioeconomic indicators were used to evaluate the sustainability of TWW reuse under multiple climate change scenarios, using the hydrologic simulation model results and statistical data. Finally, sustainability scores under multiple scenarios were aggregated using the VIKOR together with the Shannon entropy-based weights for the robust prioritization of adaptation strategies. According to the different levels of regret aversion or affinity, our results for water quality showed different sets of adaptation strategies as the best options, suggesting that our framework would help stakeholders seeking the robust options considering both the utility and regret. [ABSTRACT FROM AUTHOR]