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Changes in the essential climate extremes indices and surface variables for the end of the twenty-first century are assessed in this study based on two transient climate change simulations, with and without land-use and land-cover changes (LULCC), but identical atmospheric forcing. The two simulations are performed with the 5th generation of the Canadian Regional Climate Model (CRCM5) driven by the Canadian Earth System Model for the (2006-2100)-Representative Concentration Pathway 4.5 (RCP4.5) scenario. For the simulation with LULCC, land-cover data sets are taken from the global change assessment model (GCAM) representing the RCP4.5 scenario for the period 2006-2100. LULCC in RCP4.5 scenario suggest significant reduction in cultivated land (e.g. Canadian Prairies and Mississippi basin) due to afforestation. CRCM5 climate projections imply a general warming by the end of the twenty-first century, especially over the northern regions in winter. CRCM5 projects more warm spell-days per year over most areas of the continent, and implicitly more summer days and tropical nights at the expense of cold-spell, frost and ice days whose number is projected to decrease by up to 40% by the end of the twenty-first century with respect to the baseline period 1971-2000. Most land areas north of 45°N, in all seasons, as well as the southeastern United States in summer, exhibit increases in mean precipitation under the RCP4.5 scenario. In contrast, central parts of the continent in summer and much of Mexico in all seasons show reduced precipitation. In addition, large areas of North America exhibit changes of 10 to 40% (depending on the season and geographical location) in the number of heavy precipitation days. Results also suggest that the biogeophysical effects of LULCC on climate, assessed through differences between the two simulations, lead to warmer regional climates, especially in winter. The investigation of processes leading to this response shows high sensitivity of the results to changes in albedo as a response to LULCC. Overall, at the seasonal scale, results show that intense afforestation may contribute to an additional 25% of projected changes. [ABSTRACT FROM AUTHOR]

In this work, the climatology, observed trends, and future projections of temperature and precipitation extremes are analysed in the drainage area of the Belo Monte Hydropower Plant in the Xingu River basin. Observed data come from gridded information for the period 1980–2013. The climate projections until the end of the 21st century are provided by the regional climate model Eta‐20 km nested to the global climate model MIROC5. Seventeen climate indicators were selected for this assessment, and statistical tests were used to evaluate the significance and magnitude of trends. A tropical climate predominates in the whole basin but with differences in the climatology of extreme temperature. The average annual rainfall (PRCPTOT) presents values between 1,500 and 2,200 mm. Remarkable contrasts of consecutive dry days (CDD) can also be seen. We found a warming signal during the examined period in much of the Xingu River basin, with an increase in the frequency of extremely warm days and nights. In the northern (south‐central) area of the basin, there is an increase (reduction) in precipitation. There is a contrasted and local distribution of detected trends in all climate extremes indices related to rainfall. CDD has displayed a considerable elevation in the south‐central area over the last decades. The study area exhibits statistically significant warming projections to the future climate. As for the precipitation projections, future changes are toward a dryer climate. We also found that dry periods may last longer in the following decades. Thus, heatwaves can be excited by subsequent days without precipitation in the basin in the future climate. The impacts of climate change on the balance of different environmental and socioeconomic sectors in this area must be wholly investigated. [ABSTRACT FROM AUTHOR]

Perturbations in extreme precipitation characteristics are investigated over the Volta Basin (VB) and its three subdomains (Sahel, Soudano-Sahel and Guinea Coast) for the early-21st (2030–2053) and mid-twenty-first centuries (2057–2080) under representative concentration pathways (RCPs) 4.5 and 8.5. Seven climate indices from the Expert Team on Climate Change Detection and Indices were selected to examine future extreme precipitation features. Owing to its performance over the West African sub region, CanESM2 model results were used with Global Precipitation Climatology Centre (GPCC v7) dataset serving as reference data. Results generally show lowering trends in extreme precipitation events over the VB. The declines in extremes were dominant in the Sahel and Soudano-Sahel zones with some degree of upsurges observed in the Guinea Coast. Spatially over the basin, wet spells (CWD) were projected to shorten under RCP 8.5 (~ 7–27 days/year) relative to RCP 4.5 (~ 8–30 days/year). Similar pattern was observed for dry spells (CDD) with ranges of ~ 64–198 days/year and ~ 61–186 days/year respectively for RCPs 4.5 and 8.5. As revealed, future alterations in precipitation events have the propensity to cause alternating drought or flood events. In this line, sustainable adaptation measures and coping strategies need to be devised in time to minimize the consequences of these events, particularly those on water resources availability and ecosystem functions and services. [ABSTRACT FROM AUTHOR]

Earth system models (ESMs) are essential for understanding the interaction between human activities and the Earth's climate. However, the computational demands of ESMs often limit the number of simulations that can be run, hindering the robust analysis of risks associated with extreme weather events. While low‐cost climate emulators have emerged as an alternative to emulate ESMs and enable rapid analysis of future climate, many of these emulators only provide output on at most a monthly frequency. This temporal resolution is insufficient for analyzing events that require daily characterization, such as heat waves or heavy precipitation. We propose using diffusion models, a class of generative deep learning models, to effectively downscale ESM output from a monthly to a daily frequency. Trained on a handful of ESM realizations, reflecting a wide range of radiative forcings, our DiffESM model takes monthly mean precipitation or temperature as input, and is capable of producing daily values with statistical characteristics close to ESM output. Combined with a low‐cost emulator providing monthly means, this approach requires only a small fraction of the computational resources needed to run a large ensemble. We evaluate model behavior using a number of extreme metrics, showing that DiffESM closely matches the spatio‐temporal behavior of the ESM output it emulates in terms of the frequency and spatial characteristics of phenomena such as heat waves, dry spells, or rainfall intensity. Plain Language Summary: Ideally, to study how damaging phenomena like heatwaves, droughts and downpours will change in the future under global warming, we would want a large number of climate model runs producing many realizations of climate futures that we can analyze and from which the new characteristics of climate extremes can be quantified. Currently, emulators can rapidly generate simulations of future climate, but often to relatively low frequencies, as decadal, annual or monthly output at best in most cases, which is insufficient for studying extreme events that occur on a daily timescale. We show how it is possible to train a machine learning model to produce daily series of temperature or precipitation from monthly averages, thus facilitating a more robust investigation into how extreme events will change in the future. Key Points: Earth system models (ESMs) are key devices for understanding how human actions will affect the future global climateComputational demands prevent us from running them for more than a handful of scenarios. ESM emulators are most commonly limited to the monthly frequencyWe present DiffESM as a data‐driven emulator of ESMs that closely matches the spatiotemporal distributions of ESMs at daily frequency [ABSTRACT FROM AUTHOR]

Drought and pluvial extremes are defined as deviations from typical climatology; however, background climatology can shift over time in a non‐stationary climate, impacting interpretations of extremes. This study evaluated trends in meteorological drought and pluvial extremes by merging tree‐ring reconstructions, observations, and climate‐model simulations spanning 850–2100 CE across North America to determine whether modern and projected future precipitation lies outside the range of natural climate variability. Our results found widespread and spatially consistent exacerbation of drought and pluvial extremes, especially summer drought and winter pluvials, with drying in the west and south, wetting trends in the northeast, and intensification of both extremes across the east and north. Our study suggests that climate change has already shifted precipitation climatology beyond pre‐Industrial climatology and is projected to further intensify ongoing shifts. Plain Language Summary: Managing water resources has become challenging due to the effect of human‐caused climate change on precipitation. This study examines trends in droughts and pluvials from the distant past (850 CE) to the projected future (2100 CE) to determine whether precipitation extremes in the modern, Industrial era and future are beyond what is typical of natural climate variability in North America. Trends were generated by merging information from tree rings, observations, and climate models using a novel statistical approach. Results indicate the widespread intensification of both drought and pluvials–especially summer drought and winter pluvials during the modern and future periods. Spatially, southern and western regions of North America are becoming drier, while the northeast is getting wetter, and central areas of North America show a wider range between drought and pluvial years. Our study suggests that anthropogenic climate change has already modified drought and pluvial extremes beyond natural, pre‐Industrial conditions and these ongoing trends are projected to intensify through the future. Key Points: This study models seasonal drought and pluvial trends, merging reconstructions, observations, and projections from 850 to 2100 CEResults show widespread exacerbation of both extremes with overall drying (wetting) in southern (northeastern) North AmericaModern drought and pluvial distributions are outside pre‐Industrial (1850) conditions, and exhibiting substantial shifts in some regions [ABSTRACT FROM AUTHOR]

Systematic, routine, and comprehensive evaluation of Earth system models (ESMs) facilitates benchmarking improvement across model generations and identifying the strengths and weaknesses of different model configurations. By gauging the consistency between models and observations, this endeavor is becoming increasingly necessary to objectively synthesize the thousands of simulations contributed to the Coupled Model Intercomparison Project (CMIP) to date. The Program for Climate Model Diagnosis and Intercomparison (PCMDI) Metrics Package (PMP) is an open-source Python software package that provides quick-look objective comparisons of ESMs with one another and with observations. The comparisons include metrics of large- to global-scale climatologies, tropical inter-annual and intra-seasonal variability modes such as the El Niño–Southern Oscillation (ENSO) and Madden–Julian Oscillation (MJO), extratropical modes of variability, regional monsoons, cloud radiative feedbacks, and high-frequency characteristics of simulated precipitation, including its extremes. The PMP comparison results are produced using all model simulations contributed to CMIP6 and earlier CMIP phases. An important objective of the PMP is to document the performance of ESMs participating in the recent phases of CMIP, together with providing version-controlled information for all datasets, software packages, and analysis codes being used in the evaluation process. Among other purposes, this also enables modeling groups to assess performance changes during the ESM development cycle in the context of the error distribution of the multi-model ensemble. Quantitative model evaluation provided by the PMP can assist modelers in their development priorities. In this paper, we provide an overview of the PMP, including its latest capabilities, and discuss its future direction. [ABSTRACT FROM AUTHOR]

A significant average difference between the observed and modeled precipitation in the warm months is registered in Odesa for 1970–2005. This difference is probably determined by complex orography and inappropriate parameterization methods for convective processes climate models. In the last 15 years (2006–2020), the average temperature has increased by about 1 °C in winter and by 2 °C in summer compared with 1970–2005. Considering decreasing precipitation during summer months, it seems that the climate of Odesa is moving towards the Mediterranean climate—warm to hot, dry summers and mild, moderately wet winters. The approach based on selecting representative simulations with minimum average bias and adjusting the choice to the present-day climate is described and applied for Odesa using data from the RCP8.5 scenario simulations of the EURO-CORDEX project and ERA5-Land reanalysis. The approach can be applied separately for monthly near-surface temperature and total precipitation, as well as jointly for these variables, and provides the satisfactory ability to select models for use in impact studies. The output variables of simulations selected are close to observed ones in recent years and are well to coincide with the ensemble-mean values in the near future, 2021–2050. On the other hand, the scatter of output variables in the selected simulations adequately describes the uncertainty of the future climate. [ABSTRACT FROM AUTHOR]

Canadian climate service providers offer projections from the Coupled Model Intercomparison Project (CMIP6) to help inform climate change mitigation and adaptation decisions. CMIP6 includes several "hot" climate models whose sensitivity to greenhouse gas forcings exceeds the likely range inferred from multiple lines of evidence. Global warming estimates assessed in the Sixth Assessment Report (AR6) of the Intergovernmental Panel on Climate Change (IPCC) were reduced by applying observational constraints on the historical rate of warming to the CMIP6 ensemble. This study assesses whether globally constrained CMIP6 projections for Canada are appreciably different from unconstrained projections. Two constraints are considered: one that removes models whose transient climate response lies outside the AR6 assessed range (TCRlikely), and the other that weights models to match the assessed distribution of equilibrium climate sensitivity (ECSall). Both constraints lead to appreciably cooler and drier projections than the unconstrained ensemble, with the strongest reductions seen in the upper end of the ensemble range, high-emissions scenario, end-of-century time period, and northern regions of Canada. In this case, constrained projections of annual mean temperature are 2°–3°C cooler than the unconstrained projections, whereas projections of annual total precipitation are typically 20%–40% drier. Appreciable differences are also detected in the ensemble median of temperature extreme indices. Based on these results, it is recommended that a constrained ensemble be considered for regional projections to avoid the "hot model" problem. Alternatively, projections can be communicated conditional on a specified level of global warming, with global constraints then used to inform the timing of the warming level exceedance. [ABSTRACT FROM AUTHOR]

The performance of the Meteorological Research Institute-Atmospheric General Circulation model version 3.2 (MRI-AGCM3.2) in simulating precipitation is compared with that of global atmospheric models registered to the sixth phase of the Coupled Model Intercomparison Project (CMIP6). The Atmospheric Model Intercomparison Project (AMIP) experiments simulated by 36 Atmospheric General Circulation Model (AGCM)s and the High Resolution Model Intercomparison Project (HighResMIP) highresSST-present experiments simulated by 23 AGCMs were analyzed. Simulations by MRI-AGCM3.2S (20-km grid size) and MRI-AGCM3.2H (60-km grid size) are included as a part of the HighResMIP highresSST-present experiments. MRI-AGCM3.2S has the highest horizontal resolution of all 59 AGCMs. As for the global distribution of seasonal and annual average precipitation, monthly precipitation over East Asia, and the seasonal march of rainy zone over Japan, MRI-AGCM3.2 models perform better than or equal to CMIP6 AMIP AGCMs and HighResMIP AGCMs. HighResMIP AGCMs (average grid size 78 km) perform better than CMIP6 AMIP AGCMs (180 km) in simulating seasonal and annual precipitation over the globe, and summer (June to August) precipitation over East Asia. MRI-AGCM3.2 models perform better than or equal to CMIP6 AMIP AGCMs and HighResMIP AGCMs in simulating extreme precipitation events over the globe. Correlation analysis between grid size and model performance using all 59 models revealed that higher horizontal resolution models are better than lower resolution models in simulating the global distribution of seasonal and annual precipitation and the global distribution of intense precipitation, and the local distribution of summer precipitation over East Asia. [ABSTRACT FROM AUTHOR]

This study conducts a comprehensive analysis of the impacts of climate change on South Korea's climate and hydrology, utilizing a Multi-Model Ensemble (MME) approach with thirteen Climate Model Intercomparison Project Phase 5 (CMIP5) models under two Representative Concentration Pathways, RCP4.5 and RCP8.5. We observed an average temperature increase of up to 3.5 °C under RCP8.5 and around 2.0 °C under RCP4.5. Precipitation patterns showed an overall increase, particularly during the summer months, with increases up to 20% under RCP8.5 and 15% under RCP4.5, characterized by more intense and frequent rainfall events. Evapotranspiration rates are projected to rise by approximately 5–10% under RCP8.5 and 3–7% under RCP4.5. Runoff is expected to increase significantly, particularly in the summer and autumn months, with increases up to 25% under RCP8.5 and 18% under RCP4.5. This research focuses on employing the Precipitation Runoff Modeling System (PRMS) to project future streamflow across South Korea, with an emphasis on both monthly and seasonal scales to understand the varying impacts of climate change on different river basins. These climatic changes have profound implications for agriculture, urban water management, and ecosystem sustainability, stressing the need for dynamic and region-specific adaptation measures. This study emphasizes the critical role of localized factors, such as topography, land use, and basin-specific characteristics, in influencing the hydrological cycle under changing climatic conditions. [ABSTRACT FROM AUTHOR]

Presently, there is no standardized framework or metrics identified to assess regional climate model precipitation output. Because of this, it can be difficult to make a one-to-one comparison of their performance between regions or studies, or against coarser-resolution global climate models. To address this, we introduce the first steps toward establishing a dynamic, yet standardized, benchmarking framework that can be used to assess model skill in simulating various characteristics of rainfall. Benchmarking differs from typical model evaluation in that it requires that performance expectations are set a priori. This framework has innumerable applications to underpin scientific studies that assess model performance, inform model development priorities, and aid stakeholder decision-making by providing a structured methodology to identify fit-for-purpose model simulations for climate risk assessments and adaptation strategies. While this framework can be applied to regional climate model simulations at any spatial domain, we demonstrate its effectiveness over Australia using high-resolution, 0.5° × 0.5° simulations from the CORDEX-Australasia ensemble. We provide recommendations for selecting metrics and pragmatic benchmarking thresholds depending on the application of the framework. This includes a top tier of minimum standard metrics to establish a minimum benchmarking standard for ongoing climate model assessment. We present multiple applications of the framework using feedback received from potential user communities and encourage the scientific and user community to build on this framework by tailoring benchmarks and incorporating additional metrics specific to their application. Significance Statement: We introduce a standardized benchmarking framework for assessing the skill of regional climate models in simulating precipitation. This framework addresses the lack of a uniform approach in the scientific community and has diverse applications in scientific research, model development, and societal decision-making. We define a set of minimum standard metrics to underpin ongoing climate model assessments that quantify model skill in simulating fundamental characteristics of rainfall. We provide guidance for selecting metrics and defining benchmarking thresholds, demonstrated using multiple case studies over Australia. This framework has broad applications for numerous user communities and provides a structured methodology for the assessment of model performance. [ABSTRACT FROM AUTHOR]

This paper presents a methodology that is designed for rapid exploratory analysis of the outputs from ensembles of climate models, especially when these outputs consist of maps. The approach formalizes and extends the technique of "intermodel empirical orthogonal function" analysis, combining multivariate analysis of variance techniques with singular value decompositions (SVDs) of structured components of the ensemble data matrix. The SVDs yield spatial patterns associated with these components, which we call ensemble principal patterns (EPPs). A unique hierarchical partitioning of variation is obtained for balanced ensembles in which all combinations of factors, such as GCM and RCM pairs in a regional ensemble, appear with equal frequency: suggestions are also proposed to handle unbalanced ensembles without imputing missing values or discarding runs. Applications include the selection of ensemble members to propagate uncertainty into subsequent analyses, and the diagnosis of modes of variation associated with specific model variants or parameter perturbations. The approach is illustrated using outputs from the EuroCORDEX regional ensemble over the United Kingdom. [ABSTRACT FROM AUTHOR]

This study assesses the ability of climate models to represent rainy season (RS) dependent climate indices relevant for agriculture and crop-specific agricultural indices in eleven African subregions. For this, we analyze model ensembles build from Regional Climate Models (RCMs) from CORDEX-CORE (RCM_hist) and their respective driving General Circulation Models (GCMs) from CMIP5 (GCM_hist). Those are compared with gridded reference data including reanalyses at high spatio-temporal resolution (≤ 0.25°, daily) over the climatological period 1981–2010. Furthermore, the ensemble of RCM-evaluation runs forced by ERA-Interim (RCM_eval) is considered. Beside precipitation indices like the precipitation sum or number of rainy days annually and during the RS, we examine three agricultural indices (crop water need (CWN), irrigation requirement, water availability), depending on the RS' onset. The agricultural-relevant indices as simulated by climate models, including CORDEX-CORE, are assessed for the first time over several African subregions. All model ensembles simulate the general precipitation characteristics well. However, their performance strongly depends on the subregion. We show that the models can represent the RS in subregions with one RS adequately yet struggle in reproducing characteristics of two RSs. Precipitation indices based on the RS also show variable errors among the models and subregions. The representation of CWN is affected by the model family (GCM, RCM) and the forcing data (GCM, ERA-Interim). Nevertheless, the too coarse resolution of the GCMs hinders the representation of such specific indices as they are not able to consider land surface features and related processes of smaller scale. Additionally, the daily scale and the usage of complex variables (e.g., surface latent heat flux for CWN) and related preconditions (e.g., RS-onset and its spatial representation) add uncertainty to the index calculation. Mostly, the RCMs show a higher skill in representing the indices and add value to their forcing models. [ABSTRACT FROM AUTHOR]

Systematic biases in general circulation models (GCM) and regional climate models (RCM) impede their direct use in climate change impact research. Hence, the bias correction of GCM-RCMs outputs is a primary step in such studies. This study compares the potential of two bias correction methods (the method from the third phase of the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP3) and Detrended Quantile Matching (DQM)) applied to the raw outputs of daily data of minimum and maximum air temperatures and precipitation, in the Cap-Bon region, from eight GCM-RCM combinations. The outputs of GCM/RCM combinations were acquired from the European branch of the coordinated regional climate downscaling experiment (EURO-CORDEX) dataset for historical periods and under two representative concentration pathway (RCP4.5 and RCP8.5) scenarios. Furthermore, the best combination of bias correction/GCM-RCM was used to assess the impact of climate change on reference evapotranspiration (ET0). Numerous statistical indicators were considered to evaluate the performance of the bias correction/historical GCM-RCMs compared to the observed data. Trends of the Hargreaves–Samani_ET0 model during the historical and projected periods were determined using the TFPMK method. A comparison of the bias correction methods revealed that, for all the studied model combinations, ISIMIP3 performs better in reducing biases in monthly precipitation. However, for Tmax and Tmin, the biases are greatly removed when the DQM bias correction method is applied. In general, better results were obtained when the HadCCLM model was used. Before applying bias correction, the set of used GCM-RCMs projected reductions in precipitation for most of the months compared to the reference period (1982–2006). However, Tmin and Tmax are expected to increase in all months and for the three studied periods. Hargreaves–Samani ET0 values obtained from the best combination (DQM/ HadCCLM) show that RCP8.5 (2075–2098) will exhibit the highest annual ET0 increase compared to the RCP4.5 scenario and the other periods, with a change rate equal to 11.85% compared to the historical period. Regarding spring and summer seasons, the change rates of ET0 are expected to reach 10.44 and 18.07%, respectively, under RCP8.5 (2075–2098). This study shows that the model can be used to determine long-term trends in ET0 patterns for diverse purposes, such as water resources planning, agricultural crop management and irrigation scheduling in the Cap-Bon region. [ABSTRACT FROM AUTHOR]

Understanding long-term variations in precipitation is crucial for identifying the effects of climate change and addressing hydrological and water management issues. This study examined the trends of the mean and four extreme precipitation indices, which are the max 1-day precipitation amount, the max 5-day precipitation amount, the consecutive wet days, and the consecutive dry days, for historical observations (1971–2000) and two future periods (2041–2060/2081–2100) under RCP2.6 and RCP8.5 emission scenarios over the Nile River Basin (NRB) at 11 major stations. Firstly, the empirical quantile mapping procedure significantly improved the performance of all RCMs, particularly those with lower performance, decreasing inter-model variability and enhanced seasonal precipitation variability. The Mann–Kendall test was used to detect the trends in climate extreme indices. This study reveals that precipitation changes vary across stations, scenarios, and time periods. Addis Ababa and Kigali anticipated a significant increase in precipitation across all periods and scenarios, ranging between 8–15% and 13–27%, respectively, while Cairo and Kinshasa exhibited a significant decrease in precipitation at around 90% and 38%, respectively. Wet (dry) spells were expected to significantly decrease (increase) over most parts of the NRB, especially during the second period (2081–2100). Thereby, the increase (decrease) in dry (wet) spells could have a direct impact on water resource availability in the NRB. This study also highlights that increased greenhouse gas emissions have a greater impact on precipitation patterns. This study's findings might be useful to decision makers as they create NRB-wide mitigation and adaptation strategies to deal with the effects of climate change. [ABSTRACT FROM AUTHOR]

Global climate models (GCMs) are important tools for understanding the climate system and how it is projected to evolve under scenario-driven emissions pathways. Their output is widely used in climate impacts research for modeling the current and future effects of climate change. However, climate model output remains coarse in relation to the high-resolution climate data needed for climate impacts studies, and it also exhibits biases relative to observational data. Treatment of the distribution tails is a key challenge in existing bias-adjusted and downscaled climate datasets available at a global scale; many of these datasets used quantile mapping techniques that were known to dampen or amplify trends in the tails. In this study, we apply the Quantile Delta Mapping (QDM) method for bias adjustment. After bias adjustment, we apply a new spatial downscaling method called Quantile-Preserving Localized-Analog Downscaling (QPLAD), which is designed to preserve trends in the distribution tails. Both methods are integrated into a transparent and reproducible software pipeline, which we apply to global, daily GCM surface variable outputs (maximum and minimum temperature and total precipitation) from the Coupled Model Intercomparison Project Phase 6 (CMIP6) experiments for the historical experiment and four future emissions scenarios ranging from aggressive mitigation to no mitigation, namely SSP1–2.6, SSP2–4.5, SSP3–7.0, and SSP5–8.5. We use the European Centre for Medium-Range Weather Forecasts (ECMWF) ERA5 temperature and precipitation reanalysis as the reference dataset over the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6) reference period of 1995–2014. We produce bias-adjusted and downscaled data over the historical period (1950–2014) and the future emissions pathways (2015–2100) for 25 GCMs in total. The output dataset is the Global Downscaled Projections for Climate Impacts Research (GDPCIR), a global, daily, 0.25 ∘ horizontal-resolution product which is publicly available and hosted on Microsoft AI for Earth's Planetary Computer (https://planetarycomputer.microsoft.com/dataset/group/cil-gdpcir/ , last access: 23 October 2023). [ABSTRACT FROM AUTHOR]

We apply a methodology to identify and count records (events of unprecedented intensity) in daily precipitation time series to two sets of data: (1) different observational and reanalysis products for recent decades and (2) twenty‐first century projections (RCP8.5 and RCP2.6 scenarios) completed with two regional climate models driven by three global climate models over nine continental‐scale domains. Comparison of the detected (or actual) number of records with the corresponding number theoretically expected in stationary climate conditions (or "reference" number of records) provides indications of trends in daily precipitation extremes, as expected in a changing climate. In particular, we measure deviations from stationary conditions using the ratio of actual to reference records (RAtR) as a basic metric. We find that the observational products provide mixed indications of precipitation record trends across regions, while in the reanalysis products and the model simulations for the historical period the RAtR value shows a prevailing increasing trend with time over most continents. The RAtR shows a consistent and pronounced increase in all RCP8.5 continental‐scale projections, when sustained warming occurs throughout the 21st century, while smaller to no significant trends are found in the RCP2.6 scenario, when the warming stabilizes after about mid‐21st century. These results are indicative of an increase in precipitation extremes with global warming as measured by the higher number of local precipitation events of unprecedented intensity compared to what expected in stationary climate conditions, although a marked variability of this response is found across different regions. Our method can have useful applications in detection and attribution of hydroclimatic extremes and in impact and vulnerability assessment studies. [ABSTRACT FROM AUTHOR]

The frequency, severity, and extent of climate extremes in future will have an impact on human well-being, ecosystems, and the effectiveness of emissions mitigation and carbon sequestration strategies. The specific objectives of this study were to downscale climate data for US weather stations and analyze future trends in meteorological drought and temperature extremes over continental United States (CONUS). We used data from 4161 weather stations across the CONUS to downscale future precipitation projections from three Earth System Models (ESMs) participating in the Coupled Model Intercomparison Project Phase Six (CMIP6), specifically for the high emission scenario SSP5 8.5. Comparing historic observations with climate model projections revealed a significant bias in total annual precipitation days and total precipitation amounts. The average number of annual precipitation days across CONUS was projected to be 205 ± 26, 184 ± 33, and 181 ± 25 days in the BCC, CanESM, and UKESM models, respectively, compared to 91 ± 24 days in the observed data. Analyzing the duration of drought periods in different ecoregions of CONUS showed an increase in the number of drought months in the future (2023–2052) compared to the historical period (1989–2018). The analysis of precipitation and temperature changes in various ecoregions of CONUS revealed an increased frequency of droughts in the future, along with longer durations of warm spells. Eastern temperate forests and the Great Plains, which encompass the majority of CONUS agricultural lands, are projected to experience higher drought counts in the future. Drought projections show an increasing trend in future drought occurrences due to rising temperatures and changes in precipitation patterns. Our high-resolution climate projections can inform policy makers about the hotspots and their anticipated future trajectories. [ABSTRACT FROM AUTHOR]

This study explores the response of the increased global warning levels (GWLs) on the spatio‐temporal characteristics of extreme precipitation and temperature events over Central Africa (CA). For this purpose, eight indices proposed by the Expert Team on Climate Change Detection and Indices have been computed based on an ensemble‐mean of simulations from the COnsortium for Small‐scale MOdelling in CLimate Mode (CCLM) regional climate model, under the Representative Concentration Pathways scenario RCP8.5. The ability of CCLM to represent the climatology of considered daily hydro‐climatic extreme indices related to both precipitation and temperature was also assessed. The results showed that despite the presence of some biases, the precipitation and temperature indices are satisfactorily represented by CCLM, with some notable improvements compared to the GCMs driving fields. The climate change signals under 1.5°C GWL threshold show mostly increases (decreases) in SDII, CDD, R95PTOT, T10, T90, WSDI, and DTR (RR1) over CA throughout the year, and these effects intensify towards a warmer world. Singularly, the strongest changes in these extreme events are generally recorded during the JJA season over the northern part of CA. The results also show on one hand a widespread decrease in mean precipitation (up to 2 mm · day−1 corresponding to ~50%) associated with the increase/decrease in CDD/RR1, and on the other hand an increase in mean temperatures (up to 4°C corresponding to ~18%) associated with the increase in both lowest and highest temperatures (T10, T90). This study suggests that the CA region will be prone to droughts and floods as well as heat waves in a warmer world and calls for climate action and adaptation strategies to mitigate the risks associated with the above changes on rain‐fed agriculture, water resource, and human health. [ABSTRACT FROM AUTHOR]

Reliable projections of monsoon mean and extreme precipitation are vital to water resource management, disaster mitigation as well as policy makings. Since the climate models have better capability in projecting mean precipitation, the relationship between mean and extreme precipitation is a crucial clue for reliable projections of extreme precipitation. However, selections of optimal models in reproducing historical mean/extreme precipitation and their relationship were rarely reported. Here, more credible projections of mean/extreme precipitation and their connections over Northern Hemisphere land monsoon domain (NHLMD) were explored after a systematical assessment of the models from Coupled Model Intercomparsion Project Phase 6 (CMIP6) and CMIP5. Results indicated that most models could well reproduce the climatology and the variation of mean precipitation over NHLMD, while large portions have underestimated the extreme precipitation threshold (EPTH) and overestimated the extreme precipitation days (EPDs). However, nearly all models captured the observed robust relationships between mean and extreme precipitation. The projected mean precipitation and EPDs exhibit coherent variations with increase trend over Asian‐North African monsoon, and decrease trend over North American monsoon. The significant positive correlations between mean precipitation and EPDs will continue during 21st century. Considering the relatively low skills and large uncertainty in the simulation and the projection of EPDs, the tight connection could be used to reliably project the future extreme precipitation over the NHLMD using the projection of monsoon mean precipitation via the selected models. [ABSTRACT FROM AUTHOR]

The main objective of this study was to evaluate the effects of increasing greenhouse gases (GHGs) and total deforestation of the Amazon on extreme rainfall events in the Amazon basin. In order to quantify these impacts, numerical experiments were performed with the Eta regional climate model forced from initial and boundary conditions from the BESM, HadGEM2‐ES, and MIROC5 earth system models. In the experiment related to the increase in GHGs, numerical simulations were performed according to the IPCC RCP8.5 scenario. The effect of deforestation was quantified via an experiment in which the forest in the Amazon basin was replaced by areas of degraded pasture in the Eta model. For the analyses of the changes in extreme rainfall events, the multi‐model ensemble technique was used. The results were evaluated in terms of anomalies relative to the sensitivity and control experiments. In the results, it was observed that in an RCP8.5‐type GHG emission scenario there is a statistically significant increase in the maximum number of consecutive days without rain, a reduction in the maximum number of consecutive rainy days, reduction in total annual precipitation, and reduction in maximum annual precipitation accumulated over one and 5 days, respectively. The results for the scenario with increased GHGs and large‐scale deforestation in the Amazon basin are similar to the RCP8.5 scenario, but the intensity of changes in climate indices is significantly greater. It was also verified that the changes in the climatic indices are strongly associated with alterations in the energy balances at the surface and, consequently, in the large‐scale circulation. In general, it can be highlighted that the climate in the Amazon region is strongly dependent on the presence of the forest. [ABSTRACT FROM AUTHOR]

This study investigated future projections of extreme precipitation (PR) over the Korean Peninsula (KP) under global warming levels of 1.5 °C and 2.0 °C (GWL 1.5 °C and 2.0 °C). The bias-corrected large ensemble of the Regional Climate Model (RCM) in the Coordinated Regional Climate Downscaling Experiment–East Asia Phase 2 was used. Under GWL 1.5 °C, the RCM multi-model ensemble (MME) predicted the extreme PR intensity (RX1day) to increase by 10.14% more than the mean PR of 4.69%. A regional difference was observed in the projection, with a larger increase over the northern KP (NKP) and southern KP (SKP) than central KP. Accordingly, the distribution of extreme PR was expected to shift with the right, and extreme events occurring once every 20 years over the SKP and NKP were expected to change to a reoccurrence of 12.56 years and 10.04 years, respectively. The mechanism of extreme PR was examined for cases from June to September. The expected increase in extreme PR per warming over the SKP and NKP was 5.64% °C−1 and 8.37% °C−1, respectively, which was close to the Clausius-Clapeyron scale (7.7% °C−1). This implies that increased moisture capability from the warming will affect the change in extreme PR. Other possible factors were investigated and the RCM MME predicted vertical instability over East Asia to continue, and moisture flux and convergence around the KP to be intensified. Meanwhile, under GWL 2.0 °C, mean PR and extreme PR were projected to increase more than under GWL 1.5 °C. [ABSTRACT FROM AUTHOR]

Compound climate extremes (here referred to compound dry–hot events and compound pluvial–hot events) result in devastating disasters which threaten water‐food‐energy security. However, in a warming scenario, the risk of occurrence, the quantification of uncertainty, and associated drivers of compound climate extremes—particularly compound pluvial–hot events—have not been fully explored. By leveraging climate model large ensembles, it is revealed that the risk of compound climate extremes is projected to increase 2–3 times over most global land masses in future Representative Concentration Pathway (RCP) 8.5 forcing compared with historical forcing. Increased risks of compound climate extremes are mainly attributed to the changes in temperature and changes in dependence between precipitation and temperature, while the change in precipitation contributing to risk of these two compound climate extremes exhibits approximately spatial complementary. In the warming world, the hot spots of compound dry–hot extremes mainly lie in Europe, South Africa, and the Amazon, while those of compound pluvial–hot extremes mostly lie in the eastern USA, eastern and southern Asia, Australia, and central Africa. These findings help stakeholders and decision makers develop a package of climate change adaptation strategies to manage and mitigate the risk of compound climate extremes. Plain Language Summary: Compound climate extremes can be disastrous for water‐food‐energy security. The risk evaluation and quantification of compound climate extremes have emerged as a critical knowledge gap. Here, using climate model large ensembles, this study demonstrates that the risk of compound climate extremes is projected to increase double to triple times over most global land masses in future Representative Concentration Pathway (RCP) 8.5 forcing compared with historical forcing. Given the increasing risk of compound pluvial–hot extremes in a warming climate being larger than compound dry–hot extremes, compound pluvial–hot extremes should also receive attention. In the future, compound pluvial–hot extremes became more intense over the eastern USA, eastern and southern Asia, Australia, and central Africa. It should be mentioned that the risk evaluation and quantification of compound climate extremes in the shorter time scales should also receive attention. Key Points: Future occurrences of compound climate extremes are remarkably increasingFuture risks of compound pluvial–hot extremes are larger than those of compound dry–hot extremesIncreased risks of compound climate extremes are attributed to changes in temperature and dependence between precipitation and temperature [ABSTRACT FROM AUTHOR]

General circulation models (GCMs) are limited in their representation of regional climates. Thus, the selection and downscaling of the most suitable models for regional/local studies are crucial prior to climate change impact studies. This study addressed the selection and downscaling of GCM models from 100 ensembles each from the Shared Socioeconomic Pathways (SSP4.5 and SSP8.5) emission scenarios from the CMIP6 archive using an advanced envelop-based selection approach for Northern Nigeria. We used 2021–2050 as the short-term and 2051–2080 as the long-term study periods. The selection approach revealed that CanESM5 models are more skilful in simulating the warm and wet season, while HadGEM3-GC31-LL in the warm and dry season, whereas MPI-ESM1-2-HR and MPI-ESM1-2-LR are skilful in the cold and dry season. Furthermore, we downscaled the three most skilled models from each season and calculated their spatial averages over Northern Nigeria to provide a more precise illustration of the temperature and precipitation patterns. Under the SSP4.5 emission scenario, the ensemble mean of the downscaled and the (raw) GCMs projected about 13% (8–17%) and 20% (11–35%) increase in average annual precipitation during the short-term and long-term periods, respectively. Similarly, for SSP8.5, the models projected about 23% (5–38%) and 41% (29–60%) increase in the average annual precipitation during short-term and long-term periods respectively. For the temperature, under SSP4.5, the GCMs projected a 1.1 °C (0.26–1.6 °C) and 2.5 °C (0.87–4.04 °C) increase in average annual temperature for short-term and long-term periods respectively. Similarly, an increase of 1.2 °C (0.01–1.78 °C) and 2.7 °C (0.01–4.3 °C) is projected for SSP8.5 during the short-term and long-term periods respectively. These findings can be used for climate impact studies in the region. [ABSTRACT FROM AUTHOR]

Simulations of daily rainfall for the region of Bologna produced by 13 climate models for the period 1850–2100 are compared with the historical series of daily rainfall observed in Bologna for the period 1850–2014 and analysed to assess meteorological drought changes up to 2100. In particular, we focus on monthly and annual rainfall data, seasonality, and drought events to derive information on the future development of critical events for water resource availability. The results show that historical data analysis under the assumption of stationarity provides more precautionary predictions for long-term meteorological droughts with respect to climate model simulations, thereby outlining that information integration is key to obtaining technical indications. [ABSTRACT FROM AUTHOR]

A statistical study was made of the temporal trend in extreme temperatures in the region of Extremadura (Spain) during the period 1981–2015 using a Regional Climate Model. For this purpose, a Weather Research and Forecasting (WRF) Regional Climate Model extreme temperature dataset was obtained. This dataset was then subjected to a statistical study using a Bayesian hierarchical spatio-temporal model with a Generalized Extreme Value (GEV) parametrization of the extreme data. The Bayesian model was implemented in a Markov chain Monte Carlo framework that allows the posterior distribution of the parameters that intervene in the model to be estimated. The role of the altitude dependence of the temperature was considered in the proposed model. The results for the spatial-trend parameter lend confidence to the model since they are consistent with the dry adiabatic gradient. Furthermore, the statistical model showed a slight negative trend for the location parameter. This unexpected result may be due to the internal and modeling uncertainties in the WRF model. The shape parameter was negative, meaning that there is an upper bound for extreme temperatures in the model. [ABSTRACT FROM AUTHOR]

Detecting trends in regional large‐scale circulation (LSC) is an important challenge as LSC is a key driver of local weather conditions. In this work, we focus on two LSC characteristics that are linked to the generation of extreme precipitation in the northern French Alps and that allow interpreting changes in flow direction and flow intensity. Considering 500 hPa geopotential height fields, we show that CNRM‐CM6‐1 climate model simulates that future circulations (2015–2100) will tend to feature stronger flows with more marked westerly component. They will visit up to 30% more often the atmospheric states that are characteristic of extreme precipitation in the northern French Alps with particularly over‐recurrence in winter (+60%) and spring (+50%). In a "fictive" world where only these characteristics would change, this would induce an overall over‐recurrence of extreme precipitation of up to 15% (30% in winter), which corresponds to about 20% of the change in extreme precipitation simulated directly by CNRM‐CM6‐1. Applying the present methodology to an ensemble of climate models appears as the next step to account for simulation uncertainties both in LSC and precipitation extremes. Plain Language Summary: Detecting trends in atmospheric circulation is an important challenge as it is a key driver of local weather conditions. In this work, we study whether Southwestern Europe future atmospheric circulation will change compared to past circulations, based on a single climate model. We focus on two atmospheric circulation characteristics that are linked to the generation of extreme precipitation in the northern French Alps. We show that the class of circulations usually generating extremes may occur up to 30% more often in 2070–2100 than in 1980–2010. This would induce an over‐recurrence of extreme precipitation of up to 15% at the end of the 21st century. However CNRM‐CM6‐1 simulates an even larger over‐recurrence of extreme precipitation in the future, implying that other features are likely at play among which the increasing humidity of warmer air. Key Points: We study the resemblance between future and past circulations focusing on specific characteristics associated to extreme precipitationCNRM‐CM6‐1 climate model simulates that future geopotentials will feature stronger flows with more marked westerly componentCNRM‐CM6‐1 simulates that the atmospheric states generating extreme precipitation will be visited up to 30% more often in the future [ABSTRACT FROM AUTHOR]

Detection and attribution experiments are designed for the causal diagnosis of features in the climate system, including trends in mean climate and extreme events. While several detection and attribution data sets now exist, the coarse resolution of the climate models used (∼100‐km) often hinders their application to topographically complex regions like Aotearoa New Zealand and small island nations. The coarse atmospheric resolution may also be detrimental for simulating certain features of the atmospheric circulation, including the jets, blocking and cyclones. To address this, here we introduce a new set of climate model runs consisting of high‐resolution atmospheric simulations from the Conformal Cubic Atmospheric Model (CCAM) non‐hydrostatic global model. The variable‐resolution grid employed by CCAM enables targeted high‐resolution simulations over New Zealand (12‐km) and intermediate resolution over the wider South Pacific region (12–35‐km). Simulations from the historical experiment (years 1982–2021), consisting of ten initial condition ensemble members, are presented and evaluated here. The evaluation focuses on the representation of the large‐scale atmospheric circulation over the Southern Hemisphere including the jet streams, storm tracks, cyclones, blocking and teleconnections, as well as more localized temperature and precipitation variability and extremes specifically over New Zealand. While certain biases are highlighted and discussed for the large‐scale atmospheric circulation, CCAM is found to perform especially well for various precipitation and temperature‐based extreme indices at smaller scales across New Zealand, generally outperforming state‐of‐the‐art reanalysis and coarser resolution global atmospheric models. These results support further application of the CCAM ensemble for studying weather and climate extremes in attribution studies. Plain Language Summary: The coarse resolution of climate model output is particularly problematic for small island nations or regions with complex terrain, like New Zealand. Here we present a new set of climate model simulations based on a stretched grid global atmospheric model which enhances the resolution over New Zealand (12‐km) and the South Pacific region (12–35‐km). A comprehensive evaluation of the model is presented, including for the jets, storm tracks, cyclones, blocking, teleconnections, and localized extremes and variability. While certain biases are highlighted at the large scale, the model ensemble is shown to perform particularly well at simulating the variability and extremes of temperature and precipitation over New Zealand. Key Points: A new perturbed initial condition ensemble of historical high‐resolution climate model simulations is presented targeting New ZealandThe jets, storm tracks, cyclones, blocking, teleconnections, and localized extremes and variability are evaluatedThe ensemble is compared against various CMIP6 and AMIP models showing encouraging results over New Zealand [ABSTRACT FROM AUTHOR]

Future precipitation changes are typically estimated from climate model simulations, while the credibility of such projections needs to be assessed by their ability to capture observed precipitation changes. Here we evaluate how skillfully historical climate simulations contributing to the Coupled Model Intercomparison Project Phase 6 (CMIP6) capture observed changes in mean and extreme precipitation. We find that CMIP6 historical simulations skillfully represent observed precipitation changes over large parts of Europe, Asia, northeastern North America, parts of South America and western Australia, whereas a lack of skill is apparent in western North America and parts of Africa. In particular in regions with moderate skill the availability of very large ensembles can be beneficial to improve the simulation accuracy. CMIP6 simulations are regionally skillful where they capture observed (positive or negative) trends, whereas a lack of skill is found in regions characterized by negative observed precipitation trends where CMIP6 simulates increases. Plain Language Summary: Climate models are the primary tools to predict future changes in precipitation related to global warming. These predictions can however only usefully inform adaptation measures if they can be trusted. Here we evaluate the trustworthiness of climate model‐simulated precipitation changes based on their capability to correctly capture observed precipitation changes. We apply skill measures commonly used for the evaluation of seasonal to decadal climate predictions to historical climate simulations. We perform this analysis for total precipitation accumulations and indicators of precipitation extremes. The level of skill differs between regions and can be sensitive to the number of available simulations, with some regions benefitting from very large simulation ensembles. Mean and extreme precipitation are skillfully predicted in similar regions, including large parts of Europe and Asia. Lack of skill typically occurs in regions where observed precipitation is characterized by downward trends but Coupled Model Intercomparison Project Phase 6 models simulate increases. This study helps understand the trustworthiness of climate simulations to realistically capture precipitation changes, identifying regions where current models are more or less capable. Key Points: Coupled Model Intercomparison Project Phase 6 (CMIP6) realistically simulates observed changes in mean and extreme precipitation in large parts of Europe and Asia and other land regionsIn regions with moderate skill and observed precipitation subject to multi‐decadal variations the availability of very large ensembles is beneficialLack of skill occurs primarily in regions where negative precipitation trends are observed but CMIP6 simulates increases [ABSTRACT FROM AUTHOR]

Current climate models project that Antarctic sea ice will decrease by the end of the twenty-first century. Previous studies have suggested that Antarctic sea ice changes have impacts on atmospheric circulation and the mean state of the Southern Hemisphere. However, little is known about whether Antarctic sea ice loss may have a tangible impact on climate extremes over the southern continents and whether ocean–atmosphere coupling plays an important role in changes of climate extremes over the southern continents. In this study, we conduct a set of fully coupled and atmosphere-only model experiments forced by present and future Antarctic sea ice cover. It is found that the projected Antarctic sea ice loss by the end of the twenty-first century leads to an increase in the frequency and duration of warm extremes (especially warm nights) over the southern continents and a decrease in cold extremes over most regions. The frequency and duration of wet extremes are projected to increase over South America and Antarctica, whereas changes in dry days and the longest dry spell vary with regions. Further Antarctic sea ice loss under a quadrupling of CO2 leads to similar but larger changes. Comparison between the coupled and atmosphere-only model experiments suggests that ocean dynamics and their interactions with the atmosphere induced by Antarctic sea ice loss play a key role in driving the identified changes in temperature and precipitation extremes over southern continents. By comparing with global warming experiments, we find that Antarctic sea ice loss may affect temperature and precipitation extremes for some regions under greenhouse warming, especially Antarctica. [ABSTRACT FROM AUTHOR]

In the coming few decades, projected increases in global temperature and humidity are generally expected to exacerbate human exposure to climate extremes (e.g., humid-heat and rainfall extremes). Despite the growing risk of humid-heat stress (measured by wet-bulb temperature), it has received less attention in East Africa, where arid and semi-arid climatic conditions prevail. Moreover, no consensus has yet been reached across models regarding future changes in rainfall over this region. Here, we screen Global Climate Models (GCMs) from CMIP5 and CMIP6 and use, for boundary conditions, simulations from only those GCMs that simulate successfully recent climatic trends. Based on these GCMs and Regional Climate Model (RCM) simulations, we project that annual mean temperature is likely to rise by 2 ℃ toward midcentury (2021–2050) ​at a faster rate than the global average (about 1.5 ℃), under the RCP8.5 and SSP5-8.5 scenarios, associated with more frequent and severe climate extremes. In particular, low-lying regions in East Africa will be vulnerable to severe heat stress, with an extreme wet-bulb temperature approaching or exceeding the US National Weather Service's extreme danger threshold of 31 ℃. On the other hand, population centers in the highlands of Ethiopia will receive significantly more precipitation during the autumn season and will see more extreme rainfall events, with implications for flooding and agriculture. The robustness of these results across all GCM and RCM simulations, and for both of CMIP5 and CMIP6 frameworks (CMIP: Coupled Model Inter-comparison Project) supports the reliability of these future projections. Our simulations of near-term climate change impacts are designed to inform the development of sound adaptation strategies for the region. [ABSTRACT FROM AUTHOR]

Climate projections from global circulation models (GCMs), part of the Coupled Model Intercomparison Project 6 (CMIP6), are often employed to study the impact of future climate on ecosystems. However, especially at regional scales, climate projections display large biases in key forcing variables such as temperature and precipitation. These biases have been identified as a major source of uncertainty in carbon cycle projections, hampering predictive capacity. In this study, we open the proverbial Pandora's box and peer under the lid of strategies to tackle climate model ensemble uncertainty. We employ a dynamic global vegetation model (LPJ-GUESS) and force it with raw output from CMIP6 to assess the uncertainty associated with the choice of climate forcing. We then test different methods to either bias-correct or calculate ensemble averages over the original forcing data to reduce the climate-driven uncertainty in the regional projection of the Australian carbon cycle. We find that all bias correction methods reduce the bias of continental averages of steady-state carbon variables. Bias correction can improve model carbon outputs, but carbon pools are insensitive to the type of bias correction method applied for both individual GCMs and the arithmetic ensemble average across all corrected models. None of the bias correction methods consistently improve the change in simulated carbon over time compared to the target dataset, highlighting the need to account for temporal properties in correction or ensemble-averaging methods. Multivariate bias correction methods tend to reduce the uncertainty more than univariate approaches, although the overall magnitude is similar. Even after correcting the bias in the meteorological forcing dataset, the simulated vegetation distribution presents different patterns when different GCMs are used to drive LPJ-GUESS. Additionally, we found that both the weighted ensemble-averaging and random forest approach reduce the bias in total ecosystem carbon to almost zero, clearly outperforming the arithmetic ensemble-averaging method. The random forest approach also produces the results closest to the target dataset for the change in the total carbon pool, seasonal carbon fluxes, emphasizing that machine learning approaches are promising tools for future studies. This highlights that, where possible, an arithmetic ensemble average should be avoided. However, potential target datasets that would facilitate the application of machine learning approaches, i.e., that cover both the spatial and temporal domain required to derive a robust informed ensemble average, are sparse for ecosystem variables. [ABSTRACT FROM AUTHOR]

Precipitation indices based on daily gauge observations are well established, openly available and widely used to detect and understand climate change. However, in many areas of climate science and risk management, it has become increasingly important to understand precipitation characteristics, variability and extremes at shorter (sub-daily) durations. Yet, no unified dataset of sub-daily indices has previously been available, due in large part to the lesser availability of suitable observations. Following extensive efforts in data collection and quality control, this study presents a new global dataset of sub-daily precipitation indices calculated from a unique database of 18,591 gauge time series. Developed together with prospective users, the indices describe sub-daily precipitation variability and extremes in terms of intensity, duration and frequency properties. The indices are published for each gauge where possible, alongside a gridded data product based on all gauges. The dataset will be useful in many fields concerned with variability and extremes in the climate system, as well as in climate model evaluation and management of floods and other risks. [ABSTRACT FROM AUTHOR]

The study projects climate over the Upper Indus Basin (UIB), covering geographic areas in India, Pakistan, Afghanistan, and China, under the two Representative Concentration Pathways (RCPs), viz., RCP4.5 and RCP8.5 by the late twenty-first century using the best-fit climate model validated against the climate observations from eight meteorological stations. GFDL CM3 performed better than the other five evaluated climate models in simulating the climate of the UIB. The model bias was significantly reduced by the Aerts and Droogers statistical downscaling method, and the projections overall revealed a significant increase in temperature and a slight increase in precipitation across the UIB comprising of Jhelum, Chenab, and Indus sub-basins. According to RCP4.5 and RCP8.5, the temperature and precipitation in the Jhelum are projected to increase by 3 °C and 5.2 °C and 0.8% and 3.4% respectively by the late twenty-first century. The temperature and precipitation in the Chenab are projected to increase by 3.5 °C and 4.8 °C and 8% and 8.2% respectively by the late twenty-first century under the two scenarios. The temperature and precipitation in the Indus are projected to increase by 4.8 °C and 6.5 °C and 2.6% and 8.7% respectively by the late twenty-first century under RCP4.5 and RCP8.5 scenarios. The late twenty-first century projected climate would have significant impacts on various ecosystem services and products, irrigation and socio-hydrological regimes, and various dependent livelihoods. It is therefore hoped that the high-resolution climate projections would be useful for impact assessment studies to inform policymaking for climate action in the UIB. [ABSTRACT FROM AUTHOR]

With global warming, the behavior of extreme precipitation shifts toward nonstationarity. Here, we analyze the annual maxima of daily precipitation (AMP) all over the globe using projections of the latest phase of the Coupled Model Intercomparison Project (CMIP6) under four shared socioeconomic pathways (SSPs). The projections were bias corrected using a semiparametric quantile mapping, a novel technique extended to extreme precipitation. This analysis 1) explores the variability of future AMP globally and 2) investigates the performance of stationary and nonstationary models in describing future AMP with trends. The results show that global warming potentially intensifies AMP. For the nonparametric analysis, the 33-yr precipitation levels are increasing up to 33.2 mm compared to the historical period. The parametric analysis shows that the return period of 100-yr historical events will decrease approximately to 50 and 70 years in the Northern and Southern Hemispheres, respectively. Under the highest emission scenario, the projected 100-yr levels are expected to increase by 7.5%-21% over the historical levels. Using stationary models to estimate the 100-yr return level for AMP projections with trends leads to an underestimation of 3.4% on average. Extensive Monte Carlo experiments are implemented to explain this underestimation. [ABSTRACT FROM AUTHOR]

Model-derived climate projections have been used by decision-makers for climate change impact assessment, adaptation, and vulnerability studies at large scale. However, they are reported to have significant bias against observed data. The accuracy of dynamically downscaled data depends on the large-scale forcings; however, they still have some systematic errors, so it requires further bias correction. Before using these data for further studies, they need to be processed for performance evaluation. This review article provides current understanding in the field of analyzing global climate projections. It includes studies from the multi-criteria decision-making approaches along with its pros/cons to the performance evaluation of climate models. Moreover, this article discusses several bias correction approaches, multi-model ensemble approaches, and their applications for climate change studies. [ABSTRACT FROM AUTHOR]

Diurnal temperature range (DTR) which reflects the difference between the daily maximum (Tmax) and minimum temperature (Tmin) is an important indication of changing climate and a critical thermal metric to assess the impact on agriculture, biodiversity, water resources, and human health. The major aim of this study is to assess the probable future spatio-temporal changes in the Tmax, Tmin, and DTR and their long-term warming trend from 2006 to 2099 under two representative concentration pathways (hereafter RCP4.5 and RCP8.5) over diverse agroclimatic regions of India. The observed data from India Meteorological Department (IMD) was used to evaluate the performance of climate models (1970–2005). The result shows a very slight underestimation in DTR by models compared to the observed. In future projections, we found a reduction in DTR (0.001 to 0.020 °C/year) partly linked to the substantial increase in Tmin (0.020 to 0.071 °C/year) than Tmax (0.031 to 0.060 °C/year) that was stronger in far twenty-first-century future under RCP8.5. The decline in DTR was profound and consistent over northern India (up to 3 °C) surrounding the Indo-Gangetic Plain, western dry region, and part of central India with the highest decline observed in winter and pre-monsoon season. However, a decline in DTR was also anticipated over the plateau, coastal, and eastern Himalayas region. Change in land use land cover (LULC) also complimented the decline in DTR. The main findings of the study advocate implementation of a robust framework for climate change adaptation strategies to mitigate adverse consequences to the natural ecosystem and human health over specific regions arising due to declining DTR. [ABSTRACT FROM AUTHOR]

Diurnal temperature range (DTR) is an important meteorological component affecting the yield and protein content of winter wheat. The accuracy of climate model simulations of DTR will directly affect the prediction of winter wheat yield and quality. Previous model evaluations for worldwide or nationwide cannot answer which model is suitable for the estimation of winter wheat yield. We evaluated the ability of the coupled model intercomparison project phase 6 (CMIP6) models to simulate DTR in the winter wheat–growing regions of China using CN05 observations. The root mean square error (RMSE) and the interannual variability skill score (IVS) were used to quantitatively evaluate the ability of models in simulating DTR spatial and temporal characteristics, and the comprehensive rating index (CRI) was used to determine the most suitable climate model for winter wheat. The results showed that the CMIP6 model can reproduce DTR in winter wheat–growing regions. BCC-CSM2-MR simulations of DTR in the winter wheat–growing season were more consistent with observations. EC-Earth3-Veg simulated the climatological DTR best in the wheat growing regions (RMSE = 0.848). Meanwhile, the evaluation for climatological DTR in China is not applicable to the evaluation of DTR in winter wheat–growing regions, and the evaluation for annual DTR is not a substitute for the evaluation of winter wheat–growing season DTR. Our study highlights the importance of evaluating winter wheat–growing regions' DTR, which can further improve the ability of CMIP6 models simulating DTR to serve the research of climate change impact on winter wheat yield. [ABSTRACT FROM AUTHOR]

The region of southern Africa (SAF) is among the most exposed climate change hotspots and is projected to experience severe impacts across multiple economical and societal sectors. For this reason, producing reliable projections of the expected impacts of climate change is key for local communities. In this work we use an ensemble of 19 regional climate model (RCM) simulations performed in the context of the Coordinated Regional Climate Downscaling Experiment (CORDEX) – Africa and a set of 10 global climate models (GCMs) participating in the Coupled Model Intercomparison Project Phase 5 (CMIP5) that were used as the driving GCMs in the RCM simulations. We are concerned about the degree to which RCM simulations are influenced by their driving GCMs, with regards to monthly precipitation climatologies, precipitation biases and precipitation change signal, according to the Representative Concentration Pathway (RCP) 8.5 for the end of the 21st century. We investigate the degree to which RCMs and GCMs are able to reproduce specific climatic features over SAF and over three sub-regions, namely the greater Angola region, the greater Mozambique region, and the greater South Africa region. We identify that during the beginning of the rainy season, when regional processes are largely dependent on the coupling between the surface and the atmosphere, the impact of the driving GCMs on the RCMs is smaller compared to the core of the rainy season, when precipitation is mainly controlled by the large-scale circulation. In addition, we show that RCMs are able to counteract the bias received by their driving GCMs; hence, we claim that the cascade of uncertainty over SAF is not additive, but indeed the RCMs do provide improved precipitation climatologies. The fact that certain bias patterns during the historical period (1985–2005) identified in GCMs are resolved in RCMs provides evidence that RCMs are reliable tools for climate change impact studies over SAF. [ABSTRACT FROM AUTHOR]

Four climate parameters (i.e. maximum, mean and minimum air temperature and precipitation amount) from 10 regional climate models, provided by the EURO‐CORDEX initiative, are adjusted using as reference the ROCADA gridded dataset. The adjustment was performed on a daily temporal resolution for the historical period (1971–2005), as well as for climate change scenarios based on two Representative Concentration Pathways (RCP4.5 and RCP8.5). The most accurately method for bias‐correction (BC) was selected following a 2‐fold cross‐validation approach, which was performed on historical data using two methods: Quantile Mapping (QMAP) and Multivariate Bias Correction with N‐dimensional probability (MBCn). The performances of the two methods are very similar when analysing the frequency distribution of each selected variable, whereas the comparison between (1) the intervariables correlation of the adjusted datasets, and (2) the reference dataset revealed much smaller differences for the dataset adjusted with the multivariate method, hence this was used for producing the BC scenario dataset. Based on the MBCn adjusted dataset, a climate change analysis over Romania was tested at the seasonal and annual scale. Overall, for the multimodel ensemble mean, at the country level, a substantial temperature increase is reported for both scenarios and no significant trend is revealed for precipitation amount. The adjusted RCMs are provided without any restrictions via an open‐access repository in netCDF CF‐1.4‐compliant file format. The BC climate models are archived at the 0.1° spatial resolution, in the WGS‐84 coordinate system, at a daily temporal resolution. [ABSTRACT FROM AUTHOR]

Extreme temperature events (ETEs) have evolved alongside the warming climate over most parts of the world. This study provides a statistical quantification of how human influences have changed the frequencies of extreme temperatures in New Zealand, depending on the synoptic weather types. We use the ensembles under pre‐industrial conditions (natural scenarios with no human‐induced changes) and present‐day conditions (anthropogenic scenarios) from the weather@home regional climate model. The ensemble simulations under these two scenarios are used to identify how human influences have impacted the frequency and intensity of extreme temperatures based on their connection to different large‐scale circulation patterns derived using self‐organizing maps (SOMs). Over New Zealand, an average two to three fold rise in frequencies of extremes occurs irrespective of seasons due to anthropogenic influence with a mean temperature increase close to 1°C. For some synoptic situations, the frequency of extremes are especially enhanced; in particular, for low‐pressure centres to the northeast of New Zealand where the frequency of occurrence of daily temperature extremes has increased by a factor of 7 between anthropogenic and natural ensembles for the winter season, though these synoptic patterns rarely occur. For low‐pressure centres to the northwest of New Zealand, we observe high temperatures frequently in both anthropogenic and natural ensembles which we expect is probably associated with warm air advection from the Tropics. The frequency of occurrence of high temperatures in these synoptic patterns has also increased by a factor of 2 between the natural and anthropogenic ensembles. For these synoptic states, the extremes are observed in the North Island and along the east coast of the country with the highest temperature along the Canterbury coast and Northland. The change between the natural and anthropogenic ensembles is largest on the west coast along the Southern Alps for all the synoptic circulation types. [ABSTRACT FROM AUTHOR]

Extreme events like flooding, droughts and heatwave are among the factors causing huge socio‐economic losses to Cameroonians. Investigating the potential response of rainfall and temperature extremes to global warming is therefore critically needed for tailoring and adjusting the country's policies. Recent datasets have been developed for this purpose within the Coordinated Output for Regional Evaluations (CORDEX‐CORE) initiative, at ~25 km grid spacing. These regional climate models were used to dynamically downscaled four global climate models participating in the Coupled Model Intercomparison Project phase 5 (CMIP5), under the optimistic and pessimistic representative concentration pathways (RCPs) 2.6 and 8.5, respectively. These models were employed in this study for characterizing the response of Cameroon's extreme precipitation and temperature events to global warming, using seven indices defined by the Expert Team on Climate Change Detection and Indices. Under global warming, the maximum number of consecutive dry (wet) days' is expected to increase (decrease). However, the annual total rainfall amount is expected to increase, mainly due to the intensification of very wet days and daily rainfall intensity. Furthermore, the temperature‐based indices reveal an increase (decrease) in the total annual hot (cold) days, and overall, changes intensify with increased radiative forcing. The high‐mitigated low‐emission pathway RCP2.6 features attenuated changes, and even sometimes adapts to reverse the sign of changes. Designing reliable policies to limit the risks associated with the above changes is required, as their socio‐economic consequences are likely to include food insecurity, heat‐related illness, population impoverishment, price rises and market instability. [ABSTRACT FROM AUTHOR]

Multimodel assembling methods have been commonly used to improve the reliability of climate projections by extracting relevant information from a large future possible climate scenarios. In this aspect, this study adopted the Reliability Ensemble Averaging (REA) approach to combine the precipitation outputs of multiple climate models over South Korea. The quality of REA weights assigned to each climate model was investigated using the Taylor diagram; both of these approaches showed a similar weight assignation mechanism. Furthermore, the REA performance was evaluated with the bias and root mean square error in view of reproducing the historical climate characteristics over the study region; both performance indices revealed that the REA substantially improved the performances of individual climate models at all weather stations. The analysis also showed that the climate models performance was not consistent in reproducing the precipitation characteristics over different seasons in South Korea. Thus, this study used the seasonal REA weights of each climate model for the precipitation projection. There is a general consensus between the individual climate simulators on the increasing trend of the projected precipitation in 2070–2099, except for a slight decreasing trend observed with IPSL-CM5A-LR over some parts of the central region. In general, the projected precipitation obtained with the REA over South Korea will vary between 5.23 and 10.78% for 2070–2100 relative to 1976–2005. The maximum and minimum projected precipitation increases were observed over the western and eastern parts of South Korea, respectively. [ABSTRACT FROM AUTHOR]

While global warming is mostly conceptualized in terms of increases in mean temperature, changes in the most extreme conditions encountered often have disproportionate impacts. Here, a measure of warming amplification is defined as the change in the highest yearly temperature (denoted TXx), representing extreme heat, minus that in the 80th percentile daily high temperature ( T max 80 ), which represents typical summer conditions. Based on the ERA5 reanalysis, over 1959–2021, warming of TXx averaged 1.56 K over land areas, whereas warming of T max 80 averaged 1.60 K. However, the population-weighted mean warming of TXx significantly exceeded warming of T max 80 (implying positive amplification) over Africa, South America, and Oceania. Where available, station temperature observations generally showed similar trends to ERA5. These findings provide a new target for climate model calibration and insight for evaluating the changing risk of temperature extremes. [ABSTRACT FROM AUTHOR]

The Indo‐Gangetic Plain (IGP) bears great agricultural importance and contributes to a major share of national GDP of India. In present study, a location‐specific comprehensive analysis of rainfall extremes over IGP, using second generation CORDEX‐CORE simulations in the present and future scenarios (under high emission RCP8.5 scenario) have been performed. Here, the high‐resolution CORDEX‐CORE simulations with International Centre for Theoretical Physics's regional climate model (RegCM4.7) have been considered for the detailed rainfall characteristics assessment. Twelve thresholds‐based climate indices have been analyzed to investigate the characteristics of rainfall extremes during three‐time slices: 1986–2005 (historical), 2041–2060 (near future) and 2080–2099 (far future). The RegCM4 projections suggested a substantial decline in mean Indian Summer monsoon rainfall (ISMR) and wet days (rainfall ≥ 1 mm; 7%–14%) over IGP under high‐emission RCP8.5 scenario. The contribution of 90th and 99th percentile days and total rainfall on wet days, seems to be get enhanced in future by 14%–35%, which implies the increase and intensification in rainfall extremes over IGP by the end of the 21st century. Further, the decline in ISMR and negligible changes in annual rainfall over IGP suggest the possible shift of monsoon regime during the later months of the year in warming climate. Thus, findings of present study may play a crucial role in predicting the ISMR and rainfall extremes over the IGP. Therefore, it can be useful for scientists and policymakers to plan and implement mitigation strategies. Plain Language Summary: In present study, an endeavor has been made to access the Indian Summer monsoon rainfall (ISMR) patterns and monsoon extremes over Gangetic plain which is fluvially fertile land and feeds around 40% Indian population. The International Centre for Theoretical Physics' regional climate model (RegCM4.7) robustly performs with various forcings in simulating mean precipitation and 12 different threshold‐based climate indices over Indo‐Gangetic Plain (IGP) during the historical period. However, the statistical analysis suggests RegCM4 with forcings EIN75 and MPI‐ESM‐MR followed by MIROC5 and Nor‐ESM performs robustly in simulating the rainfall patterns over the IGP. Further, various forcing combinations have produced some rainfall indices with slight differences. Therefore, a ranking‐based framework has been proposed in order to choose best forcing‐combination for future projections. A closure encounter revealed that substantial decline in mean ISMR while negligible changes in annual rainfall have been projected which resembles to the fact of shift in monsoon regime toward the later months of the year under warming climate. As far as monsoon extreme are concerned, a rise in heavy rainfall days and their intensity have been projected. Also, maximum enhancement in projected heavy rainfall characteristics have been found to be near Himalayan foothills which is may be due the increased aerosol accumulation over IGP. Key Points: The RegCM4 performs satisfactorily in simulating mean and extreme monsoonal rainfall characteristics over Gangetic PlainsProjected decline in mean monsoon rainfall whereas a rise in extreme rainfall indices over the Gangetic plain under a warming climateProjected increase in extreme rainfall indices over upper Indo‐Gangetic Plain and Himalayan foothills during future time slices under RCP8.5 scenario [ABSTRACT FROM AUTHOR]

Climate change is predicted to lead to major changes in terrestrial ecosystems. However, substantial differences in climate model projections for given scenarios of greenhouse gas emissions continue to limit detailed assessment. Here we show, using a traditional Köppen–Geiger bioclimate classification system, that the latest CMIP6 Earth system models actually agree well on the fraction of the global land surface that would undergo a major change per degree of global warming. Data from "historical" and "SSP585" model runs are used to create bioclimate maps at various degrees of global warming and to investigate the performance of the multi-model ensemble mean when classifying climate data into discrete categories. Using a streamlined Köppen–Geiger scheme with 13 classifications, global bioclimate classification maps at 2 and 4 K of global warming above a 1901–1931 reference period are presented. These projections show large shifts in bioclimate distribution, with an almost exclusive change from colder, wetter bioclimates to hotter, drier ones. Historical model run performance is assessed and examined by comparison with the bioclimatic classifications derived from the observed climate over the same time period. The fraction (f) of the land experiencing a change in its bioclimatic class as a function of global warming (ΔT) is estimated by combining the results from the individual models. Despite the discrete nature of the bioclimatic classification scheme, we find only a weakly saturating dependence of this fraction on global warming f = 1-e-0.14ΔT , which implies about 13 % of land experiencing a major change in climate per 1 K increase in global mean temperature between the global warming levels of 1 and 3 K. Therefore, we estimate that stabilizing the climate at 1.5 K rather than 2 K of global warming would save over 7.5 million square kilometres of land from a major bioclimatic change. [ABSTRACT FROM AUTHOR]

Data scarcity globally has impeded our understanding of hydrological processes. This study was aimed at evaluating skills of models in reproducing past climate in the Shire River Basin (SRB) in Malawi for future climate impact assessments. The study used observed and simulated data by Global Climate Models (GCMs) participating in the Coupled Model Intercomparison Project Phase 5 (CMIP5). A total of 52 models were considered comprising a mixture of models in the representative concentration pathways of RCP4.5 and RCP6.0. The mean annual bias, correlation, extreme precipitation indices obtained from the RClimdex package of R software program, and frequency distributions were used to quantify the accuracy of the GCM simulations. On the precipitation indices, emphasis was placed on the frequency indices (number of heavy precipitation days (RR ≥ 10 mm), R10mm; number of very heavy precipitation days (RR ≥ 20 mm), R20mm; number of extremely heavy precipitation days (RR ≥ 25 mm), R25mm; consecutive dry days (RR < 1 mm), CDD; and consecutive wet days (RR ≥ 1 mm), CWD) and on the intensity indices (daily maximum precipitation, RX1day; 5-day maximum precipitation, RX5days; annual total wet-day precipitation, PRCPTOT; and very wet days, (R95P)). Study results have revealed that there is variation in the performances of individual models and that the overall performance of the models over the SRB is generally low. Some individual models perform better than the multi-model ensemble. Results have also shown the better performance of the following models: ACCESS1-3_rcp45_r1i1p1, BNU-ESM_rcp45_r1i1p1, CSIRO-Mk3-6-0_rcp45_r3i1p1, CSIRO-Mk3-6-0_rcp45_r8i1p1, and GFDL-ESM2G_rcp45_r1i1p1 of medium–low emission pathway, RCP4.5, in replicating the historical extreme precipitation for Shire River Basin. [ABSTRACT FROM AUTHOR]

Changes in climate affect the hydrological regime of rivers worldwide and differ with geographic location and basin characteristics. Such changes within a basin are captured in the flux of water and sediment at river mouths, which can impact coastal productivity and development. Here, we model discharge and sediment yield of the Skeena River, a significant river in British Columbia, Canada. We use HydroTrend 3.0, two global climate models (GCMs), and two representative concentration pathways (RCPs) to model changes in fluvial fluxes related to climate change until the end of the century. Contributions of sediment to the river from glaciers decreases throughout the century, while basin-wide overland and instream contributions driven by precipitation increase. Bedload, though increased compared to the period (1981–2010), is on a decreasing trajectory by the end of the century. For overall yield, the model simulations suggest conflicting results, with those GCMs that predict higher increases in precipitation and temperature predicting an increase in total (suspended and bedload) sediment yield by up to 10% in some scenarios, and those predicting more moderate increases predicting a decrease in yield by as much as 20%. The model results highlight the complexity of sediment conveyance in rivers within British Columbia and present the first comprehensive investigation into the sediment fluxes of this understudied river system. [ABSTRACT FROM AUTHOR]

The ability of 42 global climate models from the Coupled Model Intercomparison Project Phase 6 (CMIP6), consisting of 20 low resolution (LR) and 22 medium resolution (MR), are evaluated for their performance in simulating mean and extreme precipitation over Indonesia. Compared to Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS), the model climatologies and interannual variability are investigated individually and as multimodel ensemble means (MME‐mean) at monthly and seasonal time scales for the historical simulation over the period 1988–2014. Overall, results show that both LR and MR CMIP6 model skills in simulating mean and extreme precipitation indices vary across specific Indonesian regions and seasons. The individual and MME‐mean tend to overestimate the observed climatology, being largest over drier regions, yet MR models perform better compared to the LR regarding the mean bias presumably due to increased resolution. CMIP6 models tend to simulate extreme precipitation better in the dry seasons compared to the wet season. The MME‐means of the LR and MR groups mostly outperform the individual models of each group in simulating wet extremes (R95p and Rx5d) but not for the dry extremes (CDD). Among the 42 CMIP6 models, three models consistently perform poorly in simulating Rx5d and R95p, namely FGOALS‐g3, IPSL‐CM6A‐LR, and IPSL‐CM6A‐LR‐INCA, and one model in consecutive dry day (CDD) simulation, MPI‐ESM‐1‐2‐HAM, and caution is warranted. Given the knowledge of such biases, the LR and MR CMIP6 climate models can be suitably applied to assist policy makers in their decision on climate change adaptation and mitigation action. [ABSTRACT FROM AUTHOR]