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Abstract Global gridded data sets of observed extremes indices underpin assessments of changes in climate extremes. However, similar efforts to enable the assessment of indices relevant to different sectors of society have been missing. Here we present a data set of sector‐specific indices, based on daily station data, that extends the HadEX3 data set of climate extremes indices. These additional indices, which can be used singly or in combinations, have been recommended by the World Meteorological Organization and are intended to empower decision makers in different sectors with accurate historical information about how sector‐relevant measures of the climate are changing, especially in regions where in situ daily temperature and rainfall data are hard to come by. The annual and/or monthly indices have been interpolated on to a 1.875° × 1.25° longitude‐latitude grid for 1901–2018. We show changes in globally‐averaged time series of these indices in comparison with reanalysis products. Changes in temperature‐based indices are consistent with global scale warming, with days with Tmax > 30°C (TXge30) increasing virtually everywhere with potential impacts on crop fertility. At the other end of the scale, the number of days with Tmin

This study was intended to evaluate the variability and trends of climate extremes by incorporating daily data from Chattogram station and from the high-resolution Coordinated Regional Climate Downscaling Experiment (CORDEX) for two different time series. Here, we also focused on evaluating the performance of the selected RCMs (CanESM2, CSIRO, and GFDL from CORDEX) using Taylor diagrams and heat map analysis. Twenty-two extreme climate indices from ETCCDI were computed for the 1950–1989 and 1990–2020 periods. Mann–Kendall and Sen's slope test were performed to estimate the trends from the indices from both station and RCMs data. Highly significant increasing trend for the warm days and warm nights’ frequencies were found, whereas the frequency of cold days and cold nights indicated a significantly decreasing trend. On the other hand, a mild increasing trend in 1-day and 5-day maximum rainfall was detected. Also, the average annual precipitation has increased by 6% from the 1950–1989 to 1990–2020 period. During the last three decades, the region has experienced heavier rainfall in the monsoon but increased water stress in the dry season. The two-fold effects of climate change on the local hydrology revealed by this study need to be addressed properly for the sustainable development of this region. HIGHLIGHTS Performance of the selected RCMs was evaluated using Taylor diagrams and Heatmaps analysis for the Chittagong region.; Variability of climate extremes from 1950–1989 to 1990–2020 was assessed.; A total of 22 extreme climate indices were computed and their changes over time were investigated.; Trend analysis was performed for the extreme rainfall and temperature indices from both station and RCMs data.;

In recent decades, the frequency and intensity of extreme weather events induced by global warming have increased significantly, exerting serious impacts on the development of society and ecosystems. Based on low (RCP2.6) and high (RCP8.5) greenhouse gas emissions scenarios from the Intergovernmental Panel on Climate Change, we use the general circulation model HadGEM2‐ES that is part of the Coupled Model Intercomparison Project Phase 5 (CMIP5) to drive the regional climate model RegCM4.7. This study evaluated the ability of both models to simulate (1986–2005) and projection (2080–2099) spatio‐temporal features of extreme indices over tropical South America (TSA). Indices based on precipitation and daily maximum and minimum temperature follow the definitions of the Expert Team on Climate Change Detection and Indices (ETCCDI). In general, the RegCM4.7 reproduces accordingly the spatial distribution of the ETCCDI indices derived from precipitation and temperature on the TSA, in addition to added value to HadGEM2‐ES, especially over regions of complex topography. HadGEM2‐ES performs poorly along the west coast, due to the topography and altitude of the Andes. On the other hand, it demonstrates better performance in the Amazon Basin compared to RegCM4.7. The projection results showed that climate extreme indices based on precipitation and temperature indicate that drought events may advance to the end of the century, showing a spatial pattern that precipitation will be gradually reduced in the Amazon Basin and Northeast region of Brazil. Despite some discrepancies between the models, studies like this one help us to understand how climate change can affect regional planning and development. [ABSTRACT FROM AUTHOR]

Historical simulations and projections of climate extremes indices of precipitation and temperature were analysed over South America until the end of the 21st century through 31 general circulation models (GCMs) under four Representative Concentration Pathways. Simulations were compared with reanalysis data, and a Bayesian inference method was used to assess the uncertainties involved in the multi‐model climate projections. Regarding the precipitation extremes indices, the GCMs' simulations reasonably approached the reanalysis data, but with heterogeneous biases, both in sign and in the location of the highest values. The temperature extremes indices presented the smallest biases when compared to precipitation. Projections show a gradual growth of precipitation extremes events as the analysed radiative forcing scenario increases, both in magnitude and extent, over a large part of South America. Projections also indicate a decrease in cold days and nights and an increase in warm days and nights, more pronounced in the equatorial region. Bayesian inference method smoothed changes in precipitation extremes events, both in magnitude and extent, compared to the simple GCMs' ensemble mean. There was no considerable variation in the temperature indices when applying the Bayesian inference. Finally, the probability density functions resulted in a predominance of multimodal and wide curves for the precipitation indices, showing great uncertainties in the GCMs' results, differently from those for the temperature indices, where the GCMs presented good agreement represented through unimodal and narrow curves. [ABSTRACT FROM AUTHOR]

Global gridded data sets of observed extremes indices underpin assessments of changes in climate extremes. However, similar efforts to enable the assessment of indices relevant to different sectors of society have been missing. Here we present a data set of sector‐specific indices, based on daily station data, that extends the HadEX3 data set of climate extremes indices. These additional indices, which can be used singly or in combinations, have been recommended by the World Meteorological Organization and are intended to empower decision makers in different sectors with accurate historical information about how sector‐relevant measures of the climate are changing, especially in regions where in situ daily temperature and rainfall data are hard to come by. The annual and/or monthly indices have been interpolated on to a 1.875° × 1.25° longitude‐latitude grid for 1901–2018. We show changes in globally‐averaged time series of these indices in comparison with reanalysis products. Changes in temperature‐based indices are consistent with global scale warming, with days with Tmax > 30°C (TXge30) increasing virtually everywhere with potential impacts on crop fertility. At the other end of the scale, the number of days with Tmin < −2°C (TNltm2) are reducing, decreasing potential damage from frosts. Changes in heat wave characteristics show increases in the number, duration and intensity of these extreme events in most places. The gridded netCDF files and, where possible, the underlying station indices are available from https://www.metoffice.gov.uk/hadobs/hadex3 and https://www.climdex.org. Plain Language Summary: To be able to assess changes in extreme temperature and rainfall events across the globe, data sets which capture characteristics of these extreme events are required. The use of indices for these characteristics further enables both data sharing and the comparison of events across the world. Extreme events have impacts across human health, our infrastructure and the natural environment. So far there has not been a global product which presents indices which are relevant for different sectors of our society, including health, agriculture and water resources. In this work we present an extension to an existing data set of extremes indices, HadEX3, by including indices defined by the World Meteorological Organization which were developed with sector specific applications in mind. We have used the same approach and methodology, and where possible the same underlying daily temperature and rainfall observations. The temperature indices show changes consistent with global scale warming, with heat wave characteristics showing increases in the number, duration and intensity of these extreme events in most places. The data files are available for use by interested researchers in their work. Key Points: We present a sector specific extension to a data set of gridded temperature and precipitation extremesThis first quasi‐global product of these indices will support decision makers in a wide range of sectorsLong‐term changes show increases in intensity, frequency, and duration of warm extremes, and corresponding decreases in cool extremes [ABSTRACT FROM AUTHOR]

This paper presents a detailed spatio‐temporal analysis of the rainfall in the state of Pernambuco, Northeast Brazil. It is based on climate indices for extreme precipitation recommended by the Expert Team on Climate Change Detection, Monitoring and Indices. To accomplish this, daily rainfall 1data (1961–2019) were extracted from 809 high‐resolution grid points (0.1° × 0.1°) using the Brazilian Daily Weather Gridded Data (BR‐DWGD). The significance and magnitude of index trends were assessed using the modified Mann–Kendall and Sen's slope tests. This study also examined whether there existed a significant difference in climate indices among the three regions (Sertão, Agreste and Zona da Mata) within the state. The findings revealed notable significant negative trends in the PRCPTOT, R10mm, R20mm, Rx1day, Rx5day and CWD indices across all regions of Pernambuco, exhibiting a gradient from the coast to the state's interior. Reduction values of up to 15 mm year−1 for PRCPTOT, 0.7 day year−1 for R10mm, 0.2 day year−1 for R20mm, 0.01 mm year−1 for Rx1day, 0.03 mm year−1 for Rx5day, 0.4 day year−1 for CWD were observed. Furthermore, an alarming pattern was also noted for CDD, displaying a higher concentration of significant positive trends in all regions of the state, with estimated increases of up to 1.4 day year−1. Conversely, a balance of trends—both positive and negative—was observed across the entire state for R95p and R99p, with a majority of trends proving non‐significant. SDII exhibited a higher frequency of grid points showing a significant positive trend, particularly notable in the Sertão and Zona da Mata regions, where significant differences in the index values were absent. However, the remaining indices showcased notable regional differences, with values decreasing from the east to the west of the state, except for CDD. This study will assist decision makers, providing detailed long‐term information essential for preventing natural disasters and supporting socioeconomic and environmental policies in the state. [ABSTRACT FROM AUTHOR]

Study region: The study area comprising Pakistan is distributed in five homogeneous climatic zones. Study focus: An integrated five step approach has been used for zonal climate extreme analysis. Seven out of thirteen most appropriate Global Climate Models (GCMs) were selected using Posterior Inclusion Probability in the Bayesian model averaging approach. The output of selected GCMs is then downscaled using statistical downscaling. Climate extremes are projected for the baseline and future time periods. Spatio-temporal trend and statistical significance analysis were performed for climate extremes. New hydrological insights for the region: Most of the climate extremes have heterogeneous trends for the precipitation under RCP4.5 and RCP8.5. The increasing trends in climate extreme are noted in the northern region, monsoon region, and south-west parts of Pakistan. Significantly increasing trend is observed in TMAXmean and TMINmean across the country. TN10P (Cool nights) and TX10P (Cool days) have decreasing trends in the future for most of the GCMs across the country for RCP4.5 and RCP8.5. In contrast, TN90P (warm nights) and TX90P (warm days) have increasing trends for all GCMs in future under both scenarios. For RCP8.5, temperature extremes have significantly increased except TN10P and TX10P indicating significantly decreasing trends. There is notable increase in the number of summer days in future under both scenarios.

A number of studies have noted that the use of distinct reference periods when comparing indices measuring the frequency of days exceeding a particular temperature percentile threshold leads to apparently different behaviour. We show that these differences arise because of the interplay between the increasing temperatures and the choice of reference period. The time series of the indicators calculated using the different reference periods are offset, as expected, but also diverge. Linear trends calculated over the same period from the same underlying data but where different reference periods have been used are substantially different if a change in climatological conditions has occurred between the two reference periods. We show this not only occurs in our simple empirical approach, but also for the averages of gridded observational and reanalysis datasets and also at a station level. This has implications for data set comparisons using trends in temperature percentile indices that are based on different reference periods. It also has implications for updates to standard reference periods used to monitor the climate.

The Climate Data Operators [1] tool kit (CDO) is a worldwide popular infrastructure software developed and maintained at the Max Planck Institute for Meteorology (MPI-M). It comprises a large number of command line operators for gridded data, including statistics, interpolation, or arithmetics. Users benefit from the extensive support facilities provided by the MPI-M and the DKRZ.As a part of the sixth phase of the Coupled Model Intercomparison Project (CMIP6), the German Federal Ministry of Education and Research (BMBF) is funding activities promoting the use of the CDOs for CMIP6 data preparation and analysis. Firstly, a new operator 'cmor' has been developed which enables the users to rewrite their data according to the CMIP6 standard. It may be piped with other CDO operators allowing users to finish CDO applications with a standardizing 'cmor' call. The web-based post processing workflow composition for CMIP6 developed at DKRZ [2] takes advantage of the cmor operator. Secondly, both the metadata model and the data model of the CDOs had to be adapted to the CMIP6 standard.In addition, activities are underway to revise some operators for climate indices in order to adopt the procedures defined by the Expert Team on Climate Change Detection and Indices (ETCCDI) for 27 climate extremes indices. As with CMIP5, the ETCCDI climate extremes indices will be part of CMIP6 model analyses due to their robustness and straightforward interpretation.[1] Schulzweida, U. (2019): CDO user guide. code.mpimet.mpg.de/projects/cdo, last access: 01.09.2019.[2] Schupfner, M. (2019): The CMIP6 Data Request WebGUI. c6dreq.dkrz.de, last access: 01.09.2019. [ABSTRACT FROM AUTHOR]

In this study, the daily maximum and minimum temperatures measured over the period 1961–2020 by 18 stations located near the Mediterranean coast are analysed to evaluate temperature trends and to compute 10 climate indices, selected among those proposed by the Expert Team on Climate Change Detection and Indices to monitor the occurrence of climate extremes of temperature. The trend analysis is performed using the Seasonal‐Kendall test. The results show statistically significant positive trends in both above‐mentioned variables throughout the Mediterranean, although the rate of warming is more marked in the minimum than in the maximum temperature and is more evident in the western portion of the Mediterranean Basin from 1990 onward. The climate extremes indices are evaluated assuming two different base periods (1961–1990 and 1991–2020). The shift forward of the base period involves a general rise in the percentile‐based thresholds used for identifying temperature extremes, as a natural consequence of the current atmospheric warming, resulting in fewer warm events and more cold events since the 1990s using a warmer/later base period. The application of the Seasonal‐Kendall test to the occurrence of extreme warm and cold events reveals that the western portion of the Mediterranean and the Adriatic Sea are the subregions most influenced by the base period update. This study demonstrates that the selection of the base period for the identification of extreme temperature events significantly impacts the results, and that the choice of a recent base period partially masks the ongoing atmospheric warming. The results suggest that the base period 1961–1990 might be more appropriate for climatological studies, as it provides a solid and stable baseline, and that attention must be paid when scientific results are shared with stakeholders, so as not to alter the communication of warnings and specific risks for the population. [ABSTRACT FROM AUTHOR]

Interpreting changes in climate extremes indices and related large-scale teleconnection factors is critical to addressing intensified climate change. The spatiotemporal trend in SU25 (summer days) and ID0 (icing days), as well as the contribution rates of large-scale circulation factors, were investigated using daily maximum temperature data from 93 stations in the Shandong Peninsula and 8 metrics of large-scale teleconnections from 1961 to 2017. The results showed that SU25 increased significantly while ID0 decreased significantly in the Shandong Peninsula during the study period. SU25 ranged from 66.3 to 155.1 days and gradually declined from inland to coast. ID0 ranged from 4.7 to 24.8 days and decreased roughly from north to south. SU25 had a significant negative correlation with longitude, while the trend of SU25 had a significant positive correlation with longitude. ID0 had a significant positive correlation with both latitude and longitude, while the trend of ID0 had a significant negative correlation with both latitude and longitude. The abrupt years of SU25 were concentrated in 1965, 1976, 1977, 1994, 2007, and 2012, respectively. The change point of ID0 at each station was most concentrated in 1970, 1972, 1986, and 1988, respectively. In the future, SU25 in most stations will continue to increase, while ID0 in almost all the stations will decrease. The Atlantic Multidecadal Oscillation (AMO) was the most important factor influencing SU25 change. Both the Arctic Oscillation (AO) and AMO made significant contributions to the ID0 change. The combined results of the two factors were usually greater than the results of the single circulation factor, and the contributions to SU25 and ID0 were mostly nonlinear enhancement. [ABSTRACT FROM AUTHOR]

One of the world's most challenging problems is climate change, which threatens many aspects of the social-ecological landscape of nature and human systems, particularly in the Horn of Africa. The study focuses on the spatiotemporal distribution of climate extremes indices under projected future climatic conditions over the Lake Tana sub-basin. Daily-time series in three socioeconomic pathways (SSP1-2.6, SSP2-4.5, and SSP5-8.5) for the near, mid, and far future periods and CMIP6 models were chosen as the best-fit models. Additionally, the climate variability of the basin is represented by eleven climate extreme indicators. The result indicates that model MPI-ESM1-2-HR gives reasonable performance for precipitation which is 0.76 coefficient of determination (R2) and 0.75 for Nash and Sutcliffe efficiency (NSE) and for maximum temperature 0.76 for R2, 0.75 for (NSE) and minimum temperature 0.65 for R2, 0.64 for NSE. Scenario SSP5-8.5 in the far future period will experience the highest warm period TXx and TXn around 33–32 °C and 21–20 °C, respectively, at stations Zegie, Addis Zemen, and Maksegnt. The coldest extremes TNn discovered at station Dangla throughout the near timeframe under the SSP2-4.5 scenario were between 5.9 and 6.7 °C. Heavy rainfall (R10) occurred in the top section of the subbasin for 26–27 days in Scenario SPP1-2.6, but exceptionally heavy rainfall (R20) was discovered in a far future period on average for 0.83–0.94 days in Scenario SSP5-8.5. The highest Rsum found was 1100–1000 mm rainfall at SSP1-2.6 at station Maksegnt in the same period. In a scenario of SSP5-8.5, dry CDD occurred at station Debretabor during a mid-future period for an average of 130–120 days. CWD was discovered during the mid-period of SSP2-4.5 in the upper portion of the basin at Maksegnt and Addis Zemen stations for an average of 120–130 days. In general, it is crucial for the stakeholders to be aware of the facts to develop and plan future activities on the basin that are related to the climate, health, and other extremes for alerting future adaptation and mitigation planning techniques to battle the effects of excessive heating in the sub-basin. [ABSTRACT FROM AUTHOR]

Understanding the spatiotemporal variations in climate extremes indices, as well as the influencing factors, is critical to the scientific response to climate change. The temporal and spatial variations of SU25 (annual count of days when daily maximum temperature > 25 °C) were discussed in this study, based on daily maximum temperature data from 2398 meteorological stations in China from 1961 to 2017. The contributions of associated large-scale circulation factors to SU25 were quantitatively assessed by using the geographical detector method (GMD). The overall spatial distribution of SU25 was marked by a considerable increase from north to south. The SU25 increased significantly over time, with the national SU25 increasing at a rate of 2.5 days/decade. The Tibet Plateau (TP) had the slowest growth rate, with an average increase rate of 1.4 days/decade. The Hurst values of SU25 in all the subregions were generally high, indicating that most stations of SU25 would continue to increase in the future. Except for TP, the tipping years of other subregions were concentrated in the 1990s, and SU25 increased after the years. Among the large-scale circulation factors affecting SU25 in each subregion, Atlantic Multidecadal Oscillation (AMO) played a major role in SU25 variability. As a whole, the result of the pairwise interaction of each circulation factor was mainly nonlinear enhancement. The joint contributions of multiple factors to SU25 were larger than the contribution of each individual factor. [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]

Despite intense research on climate change (CC), regional studies for Central America, which is considered a CC hot spot, remain scarce. The information provided by general circulation models (GCMs) is too coarse to accurately reproduce local‐scale climatic features, which are needed for impact assessment. Thus, downscaling techniques are employed to address this scale mismatch. Costa Rica is the present case study, for which suitable predictors were tailored for downscaling related to regional climatic characteristics, such as the Inter‐Tropical Convergence Zone, El Niño Southern Oscillation, the Caribbean Low‐Level Jet, and the Mid‐Summer Drought. Statistical downscaling models were calibrated for precipitation, maximum and minimum temperature, using the perfect prognosis methodology by means of station data, ERA‐INTERIM reanalysis and artificial neural networks, yielding satisfactory results. As found in several studies, the temperature models replicated more accurately the statistics of the observed datasets. However, here, through the implemented approach and the tailored predictors, the precipitation models conveyed an improvement compared to standard methods. Projected daily climate was obtained employing CORDEX data under the RCP8.5 scenario for the central region of the country. Overall, the changes in climate estimated by the end of the 21st century agree with coarser‐scale projections. Finally, projected climate extremes indices were calculated and rendered further details on the intensity of future CC by the end of the century. [ABSTRACT FROM AUTHOR]

A broad spectrum of model-derived weather datasets are available in the US. Because each product integrates atmospheric conditions with different model processes, each produces different statistical biases. This study validated air temperature from NLDAS-2 and a novel statistically downscaled NLDAS-2 against observational weather station data for the state of Florida. We statistically downscaled NLDAS-2 to a 1-km grid product using MODIS land surface temperature. We investigated mean biases and Pearson correlation coefficients between daily observational data and the two model-derived datasets. We then calculated multiple Climate Extremes Indices to further scrutinize differences in capturing extreme temperatures. Finally, we quantified potential causes of systematic NLDAS-2 biases related to distance from the coast, urban heat island, land cover, and type of observational stations. Two model-derived datasets showed similar mean biases and correspondence with observational data, underestimating maximum temperature by 1°C and overestimating minimum temperature by 2°C. Extreme temperatures were well simulated in both datasets. However, we still found overestimated extreme minimum temperatures and underestimated extreme maximum temperatures. Systematic biases tended to be higher for coastal stations and grids having a high fraction of water. Our study suggests that including physical processes covering land surface and ocean interactions may improve the model accuracy. [ABSTRACT FROM AUTHOR]

ABSTRACT This paper documents the changes in climate extremes over Indonesia during the past three decades (1983-2012) based on a subset of extreme indices recommended by the Expert Team on Climate Change Detection and Indices ( ETCCDI). The extreme indices were calculated based on the quality controlled daily observational data (minimum and maximum temperature and precipitation) from 88 weather stations. Overall, we found significant and spatially coherent trends of warming in the temperature indices over Indonesia, consistent with other studies conducted at different countries within the Southeast Asia. The frequency of cool days ( TX10p) and cools nights ( TN10p) had decreased whereas warm days ( TX90p) and warm nights ( TN90p) were observed more frequently. Averaged over the country, the annual mean of daily maximum ( TXmean) and minimum temperature ( TNmean) had increased significantly by 0.18 and 0.30 °C decade−1, respectively. Other temperature indices also showed significant warming trends. In contrast, trends in the precipitation extremes indices were generally not significant and less spatially coherent. However, a tendency towards wetter conditions was observed, in agreement with the results at the global scale. The daily precipitation intensity ( SDII) had increased significantly over the country by 0.21 mm day−1 decade−1 during the period studied. At the regional scale, we observed a significant wetting trend in the annual highest daily amount ( RX1day) and the rainfall amount contributed by the extremely very wet days (R99p) in the northern part of the country. The wetting trends of a number of extreme precipitation indices were depicted prominently in December-January-February ( DJF) and/or March-April-May ( MAM) seasons, both at country and regional levels. However, for the southern region of Indonesia, a drying tendency was observed for June-July-August ( JJA), September-October-November ( SON) and MAM. [ABSTRACT FROM AUTHOR]

Central America and the Caribbean are regularly battered by megadroughts, heavy rainfall, heat waves, and tropical cyclones. Although 21st-century climate change is expected to increase the frequency, intensity, and duration of these extreme weather events (EWEs), their incidence in regional protected areas (PAs) remains poorly explored. We examined historical and projected EWEs across the region based on 32 metrics that describe distinct dimensions (i.e., intensity, duration, and frequency) of heat waves, cyclones, droughts, and rainfall and compared trends in PAs with trends in unprotected lands. From the early 21st century onward, exposure to EWEs increased across the region, and PAs were predicted to be more exposed to climate extremes than unprotected areas (as shown by autoregressive model coefficients at p < 0.05 significance level). This was particularly true for heat waves, which were projected to have a significantly higher average (tested by Wilcoxon tests at p < 0.01) intensity and duration, and tropical cyclones, which affected PAs more severely in carbon-intensive scenarios. PAs were also predicted to be significantly less exposed to droughts and heavy rainfall than unprotected areas (tested by Wilcoxon tests at p < 0.01). However, droughts that could threaten connectivity between PAs are increasingly common in this region. We estimated that approximately 65% of the study area will experience at least one drought episode that is more intense and longer lasting than previous droughts. Collectively, our results highlight that new conservation strategies adapted to threats associated with EWEs need to be tailored and implemented promptly. Unless urgent action is taken, significant damage may be inflicted on the unique biodiversity of the region., (© 2024 The Authors. Conservation Biology published by Wiley Periodicals LLC on behalf of Society for Conservation Biology.)

The occurrence of extreme climate events (ECEs) in the Amazon basin (AMZ) and northeast Brazil (NEB), such as torrential rains and severe droughts, varies in both spatial and temporal scales. Spatial analysis of trends allows observing changes in behaviour and determining in which regions a particular variable has been experiencing changes over time. Thus, the objective of this study is to analyse trends of 21 climate extremes indices, relative to maximum and minimum precipitation and temperature, as defined by the World Meteorological Organization, for the AMZ and NEB. The 21 indices were selected according to meteorological and climate characteristics of the regions. Through annual analysis it was possible to observe an increase in most of the climate extremes indices for air temperature in all AMZ and NEB subregions. As for extreme precipitation, only a few of the selected indices presented significant increase and/or decrease in their values. Overall, the eastern Amazon subregion in the AMZ presented the highest significant indices for temperature and precipitation. In NEB, both Northern Coast and Southern Coast subregions presented substantial increase or decrease in precipitation and temperature indices. Location of the reference points selected for each of the 11 homogeneous precipitation subregions in the AMZ and the NEB. [ABSTRACT FROM AUTHOR]

The most disastrous heatwaves are very extreme events with return periods of hundreds of years, but traditionally, climate research has focussed on moderate extreme events occurring every couple of years or even several times within a year. Here, we use three Earth System Model large ensembles to assess whether very extreme heat events respond differently to global warming than moderate extreme events. We find that the warming signal of very extreme heat can be amplified or dampened substantially compared to moderate extremes. This modulation is detectable already in mid-century projections. In the mid-latitudes, it can be explained by changes in event soil moisture-temperature coupling during the hottest day of the year. The changes depend on the interplay of present soil moisture and coupling during heat events as well as projected precipitation changes. This mechanism is robust across models, albeit with large spatial uncertainties. Our findings are highly relevant for climate risk assessments and adaptation planning. Climatic changes in extreme events are well studied, but research has so far focused on moderate extremes. The authors show that changes in very extreme heat events can be, depending on the region, much stronger or weaker than changes in moderate extremes. [ABSTRACT FROM AUTHOR]

Diagnosing climate variability and environmental change in floodable regions is essential for understanding and mitigating impacts on natural ecosystems. Our objective was to characterize environmental degradation in the Brazilian Pantanal by identifying changes in vegetation and water cover over a 30-year period using remote sensing techniques. We evaluated surface physical–hydric parameters, including Land Use and Land Cover (LULC) maps, Normalized Difference Vegetation Index (NDVI), Modified Normalized Difference Water Index (MNDWI), Normalized Difference Moisture Index (NDMI), and precipitation data. There was a decrease in the area of water bodies (−9.9%), wetlands (−5.7%), and forest formation (−3.0%), accompanied by an increase in the area of pastureland (7.4%). The NDVI showed significant changes in vegetation cover (−0.69 to 0.81), while the MNDWI showed a decrease in water surface areas (−0.73 to 0.93) and the NDMI showed a continuous decrease in vegetation moisture (−0.53 to 1). Precipitation also decreased over the years, reaching a minimum of 595 mm. Vegetation indices and land use maps revealed significant changes in vegetation and loss of water bodies in the Pantanal, reinforcing the need for sustainable management, recovery of degraded areas, and promotion of ecotourism to balance environmental conservation and local development. [ABSTRACT FROM AUTHOR]

While historical observations reveal increases in meteorological drought frequency across China during 1960–2021, future drought characteristics at the regional scale are uncertain. We evaluate historic and twenty-first-century-projected dryness changes using the NASA Earth Exchange Global Daily Downscaled Projections (NEX-GDDP) 0.25° resolution dataset for representative concentration pathways (RCPs) 4.5 and 8.5. The 20-model NEX-GDDP ensemble mean precipitation and derived potential evapotranspiration, which is calculated by utilizing the Thornthwaite approach, are consistent with gridded station data over China from 1961 to 2005. Results show that the projections of occurrence frequency of moderate drought based on the standardized precipitation evapotranspiration index (SPEI) increase remarkably under the RCP4.5 scenario, and a significant increasing frequency is seen for severe drought under the RCP8.5 scenario, while few droughts of extreme dryness occur during the twenty-first century under both emission scenarios. More moderate drought projections are found over northwestern, northern, and southeastern regions under the RCP4.5 scenario, but all of China will face severe droughts for RCP8.5. The areas affected by moderate droughts begin to extend rapidly around 2030s at both national and regional scales for the RCP4.5 scenario, and the substantial increases in drying area extension for RCP8.5 appear around the 2050s. Particularly, the severe drought areas are found to grow much more sharply after the 2070s under the RCP8.5 scenario, implying that the occurrences of severe droughts over China are more sensitive to greenhouse gas emissions. Significance Statement: Both temperature and rainfall are closely associated with drought variability; therefore, this study examines the changes in frequency and intensity of projected droughts across China based on the standardized precipitation index (SPI) and standardized precipitation evapotranspiration index (SPEI). Severe droughts are projected to increase over most of the regions according to SPEI. The substantial increases in moderate and severe droughts are seen over all of China under the RCP4.5 and RCP8.5 scenarios, respectively. At the temporal scale, most of the areas are projected to experience more severe droughts by the end of twenty-first century, while the areas of moderate droughts are projected to exhibit fluctuating variations during the twenty-first century. These results indicate more future droughts, which may increase the vulnerability of more areas of China from exposure to the consequences of global warming. [ABSTRACT FROM AUTHOR]

This paper analyzes the heavy rainfall on 16–20 May 2021 across southeastern Texas and southern Louisiana. In Louisiana, five fatalities were attributed to the event and > $1 billion in losses. The rainfall was caused by two slow-moving, mesoscale convective vortices that concentrated precipitation south of Port Arthur, TX, Lake Charles, LA, and Baton Rouge, LA, in 6 to 12-hour periods. Using calibrated radar data, the Storm Precipitation Analysis System estimated 6-h rainfall totals of 254–362 mm south of Lake Charles, surpassing 200–500-year average recurrence intervals (ARI) for those locations, with an isolated area receiving 367.5 mm, equating to a > 800-year ARI. In East Baton Rouge Parish, LA, 203–268 mm was estimated in 6-h (200–500-year ARI), with an isolated area receiving 274 mm, equivalent to a 750-year ARI. A comparison of the May 2021 and August 2016 events, which both affected southern Louisiana, is made using a depth-area-duration analysis. While the forcing mechanisms differed, 1–6 h rainfall depths were similar at 1 –25,900 km2. The May 2021 event exceeded rainfall depths produced by the August 2016 event for 2–6 h durations for area sizes of 1–388 km2, highlighting the precipitation intensity produced by the May 2021 event. [ABSTRACT FROM AUTHOR]

South America has experienced significant changes in climate patterns over recent decades, particularly in terms of precipitation and temperature extremes. This study analyzes trends in climate extremes from 1979 to 2020 across South America, focusing on their relationships with sea surface temperature (SST) anomalies in the Pacific and Atlantic Oceans. The analysis uses precipitation and temperature indices, such as the number of heavy rainfall days (R10mm, R20mm, R30mm), total annual precipitation (PRCPTOT), hottest day (TXx), and heatwave duration (WSDI), to assess changes over time. The results show a widespread decline in total annual precipitation across the continent, although some regions, particularly in the northeast and southeast, experienced an increase in the intensity and frequency of extreme precipitation events. Extreme temperatures have also risen consistently across South America, with an increase in both the frequency and duration of heat extremes, indicating an ongoing warming trend. The study also highlights the significant role of SST anomalies in both the Pacific and Atlantic Oceans in driving these climate extremes. Strong correlations were found between Pacific SST anomalies (Niño 3.4 region) and extreme precipitation events in the northern and southern regions of South America. Similarly, Atlantic SST anomalies, especially in the Northern Atlantic (TNA), exhibited notable impacts on temperature extremes, particularly heatwaves. These findings underscore the complex interactions between SST anomalies and climate variability in South America, providing crucial insights into the dynamics of climate extremes in the region. Understanding these relationships is essential for developing effective adaptation and mitigation strategies in response to the increasing frequency and intensity of climate extremes. [ABSTRACT FROM AUTHOR]

ABSTRACT: Observational analyses of changing climate extremes over the West Africa region have been limited by the availability of long and high‐quality datasets. To help address this gap, a climate extremes indices workshop was held in the Gambia in December 2011 with participants from 14 West African countries. The resulting analysis utilized 15 annual indices derived from observed daily temperatures and 10 annual indices derived from observed daily precipitation. The analysis was conducted for 166 meteorological stations in 13 countries for 2 periods: 1960–2010 and 1981–2010. The analyses of trends in the annual mean temperature indices have identified statistically significant increases of 0.16 °C/decade and 0.28 °C/decade for mean annual maximum and mean annual minimum temperatures, respectively, averaged over all available land stations in the region during the last 50 years. The seasonal‐temperature‐related indices show significant patterns of warming in all seasons. The annual mean of daily minimum temperature has increased more than the annual mean of daily maximum temperature leading to a decreasing trend in the diurnal temperature range. Warm days and warm nights have become more frequent, and cold days and cold nights have become less frequent. The analyses of precipitation‐based indices indicate spatially non‐coherent changes throughout the study area with few statistically significant trends over the longer period. Exceptions to this are the simple daily intensity index and maximum 5‐day precipitation, which show significant increasing regional trends over both the shorter and longer periods. Additionally, over the recent period (1981–2010) most of the precipitation related indices show significant trends towards wetter conditions. However, this period of increased rainfall follows a decade of significantly drier conditions in the region – it is not clear whether the recent upward trends reflect the ‘recovery’ from this long drought period or represents a long‐term response to warming. [ABSTRACT FROM AUTHOR]

With an estimated 357.7 million internal displacements caused since 2008, weather-related disasters are a major driver of human mobility worldwide. As climate change is projected to increase the frequency and intensity of extreme weather events in many parts of the world, it is important to better understand how trends in weather patterns related to global warming have affected the intensity of disasters that have caused displacements. Here we combined observational and counterfactual climate data with global internal displacement records to estimate how climate change has affected precipitation and wind speeds at the time and location of floods and storms that led to internal displacements. We estimate that, on average, climate change increased precipitation and decreased wind speeds during such events by +3.7% and − 1.4%, respectively. However, the variability across events is considerable (±28.6 and ± 6.6%, respectively), highlighting the large signal of natural variability of the weather system as compared to the global warming signal. Our results caution against overstating the role of climate change in displacement-inducing disasters in the past, especially compared to socio-economic and development factors of vulnerability and adaptive capacity that determine whether weather-related hazards turn into disasters.' [ABSTRACT FROM AUTHOR]

Understanding precipitation properties at regional scales and generating reliable future projections is crucial in providing actionable information for decision-makers, especially in regions with high vulnerability to climate change, where future changes impact ecosystem resilience, biodiversity, agriculture, water resources and human health. The South America Convection-Permitting Regional Climate Model experiment (SA-CPRCM) examines climate change effects in convection-permitting simulations at 4.5 km resolution, on climate time scales (10 year present-day and 10-year future RCP8.5 around 2100), over a domain covering most of South America, using the Met Office Unified Model (UM) convection-permitting RCM. Under the RCP8.5 scenario, precipitation in the CPRCM decreases, becomes less frequent and more seasonal over the Eastern Amazon region. Dry spells lengthen, increasing the risk of drought. In the Western Amazon, precipitation increases in the wetter austral autumn (Apr. – Jun.) and decreases in the drier austral winter and spring (July – Oct.), leading to a more distinct dry season and imposing a greater risk of contraction of the tropical forest. Over South-eastern Brazil, future precipitation increases and becomes more frequent and more intense, increasing the risk of floods and landslides. A future increase in the intensity of precipitation and extremes is evident over all these regions, regardless of whether the mean precipitation is increasing or decreasing. The CPRCM and its driving GCM respond in a similar way to the future forcing. The models produce broadly similar large-scale spatial patterns of mean precipitation and comparable changes to frequency, intensity, and extremes, although the magnitude of change varies by region and season. [ABSTRACT FROM AUTHOR]

This study aims to detect changes in rainfall variability caused by climate change for various scenarios in the CMIP6 (Coupled Model Intercomparison Project Phase 6) multi-model ensemble. Projected changes in rainfall unevenness in terms of different timescale indices using three categories, namely WD50 (number of wettest days for half annual rainfall), SI (seasonality index), and DWR (ratio of dry-season to wet-season rainfall) are analyzed in Zengwen Reservoir watershed, southern Taiwan over near future (2021–2040) and midterm future (2041–2060) relative to the baseline period (1995–2014) under SSP2-4.5 and SSP5-8.5 scenarios. The projected rainfall for both baseline and future periods is derived from 25 GCMs (global climate models). The results indicate that noticeably deteriorated rainfall unevenness is projected in the Zengwen Reservoir watershed over future periods, which include decreased WD50, increased SI, and decreased DWR. Though there were noticeable differences in the rainfall projections by the different GCMs, the overall consensus reveals that uncertainties in future rainfall should not be ignored. In addition, WD50 has the greatest deviated relative change in mean, which implies that the short-timescale rainfall unevenness index is easily affected by climate change in the study area. Distributional changes in rainfall unevenness determined by simultaneously considering alterations in relative changes in mean and standard deviation indicated that there was no single dominant category. However, the top two categories, with summed frequencies exceeding 0.5, characterize different properties of rainfall unevenness indices. The top two categories of WD50 and SI commonly have decreased mean and increased mean, respectively, but nearly equal frequencies of the top two categories in DWR exhibit opposite variations. The proposed rainfall unevenness change detection approach provides a better understanding of the impacts of climate change on rainfall unevenness, which is useful for preparing adaptive mitigation measures for coping with disasters induced by climate change. [ABSTRACT FROM AUTHOR]

Shifting precipitation regimes are a well-documented and pervasive consequence of climate change. Subsistence-oriented communities worldwide can identify changes in rainfall patterns that most affect their lives. Here we scrutinize the importance of human-based rainfall observations (collated through a literature review spanning from 1994 to 2013) as climate metrics and the relevance of instrument-based precipitation indices to subsistence activities. For comparable time periods (1955-2005), changes observed by humans match well with instrumental records at same locations for well-established indices of rainfall (72% match), drought (76%), and extreme rainfall (81%), demonstrating that we can bring together human and instrumental observations. Many communities (1114 out of 1827) further identify increased variability and unpredictability in the start, end, and continuity of rainy seasons, all of which disrupt the cropping calendar, particularly in the Tropics. These changes in rainfall patterns and predictability are not fully captured by existing indices, and their social-ecological impacts are still understudied.This paper provides an analysis or rainfall changes that integrates human-based and instrument-based observations, identifying regions of agreement and disagreement, and providing insights on how traditional ecological knowledge can complement instrumental indices. [ABSTRACT FROM AUTHOR]

In the context of increasingly frequent and severe climate extremes, an understanding of the impacts of these events on gross primary production (GPP) and thus on land carbon uptake is crucial. However, research utilizing new model outputs to assess the future trends, characteristics, and driving factors of GPP reduction associated with extreme events remains limited. Here, we use model outputs from Phase Six of the Coupled Model Intercomparison Project (CMIP6) to investigate the spatiotemporal patterns of negative GPP extreme events during the 21st century. We find a notable increase in negative GPP extremes globally under the SSP5‐8.5 scenario. They are characterized by longer durations and larger sizes, despite the smaller number of events. Under the SSP1‐2.6 scenario, while the total negative GPP extremes remain relatively stable, hotspots, including tropical forests, southern China, and boreal forest zones, still experience increases in negative extremes. By attributing these GPP extremes to climate conditions, we identified compound hot and dry conditions, which contributed to over 40% of the negative GPP extremes under both scenarios, as the dominant driver, followed by single‐driver dry conditions. Under SSP5‐8.5, the increasing contribution of compound hot and dry conditions leads to greater GPP reductions through prolonged and intensified negative extreme events. Compared with CMIP5 models, CMIP6 models project an asymmetry of negative and positive GPP extreme events that favors more negative extremes across most regions. Our findings highlight the escalating damage from climate extremes on future ecosystem productivity, emphasizing the urgent need for effective mitigation and adaptation actions. Plain Language Summary: In a world facing more frequent and severe climate extremes, an understanding of how these events affect plant growth and the uptake of CO2 by terrestrial ecosystems is crucial. Our study uses outputs from Earth system models in Coupled Model Intercomparison Project (CMIP6) to analyze the future trends and driving forces of negative extreme events in gross primary production (GPP). We found that under a high‐emission scenario (SSP5‐8.5), global GPP loss linked to negative extreme events of increasing severity and duration almost doubled by the end of the 21st century compared with current levels. Under a strong mitigation scenario (SSP1‐2.6), negative GPP extremes increase in regions such as tropical forests, southern China, and boreal forest zones despite a relatively stable global total GPP loss. Compound hot and dry conditions, the main driver of negative GPP extremes, contributed to more than 40% of the GPP extremes under both scenarios. Compared with those in CMIP5, the latest models in CMIP6 project more negative GPP extremes than positive extremes across most regions. These results emphasize the urgent need for action to mitigate and adapt to the escalating damage caused by climate extremes on future ecosystem productivity. Key Points: A notable increase in negative gross primary production (GPP) extremes is found under SSP5‐8.5, characterized by longer durations and greater magnitudes of eventsCompound hot and dry conditions are the dominant driver, contributing to >40% of the negative GPP extremes under both SSP5‐8.5 and SSP1‐2.6Compared with CMIP5, the Coupled Model Intercomparison Project (CMIP6) models project more negative GPP extremes than positive extremes across most regions [ABSTRACT FROM AUTHOR]

Global warming has led to an increasing frequency and intensity of extreme precipitation events worldwide. The extreme precipitation of Henan Province in central China usually occurs in summer, with the climate transition from the northern subtropical to the warm temperate climate. Compared with the study of extreme precipitation events in other regions, the study of Henan Province pays less attention. In order to systematically understand the spatial and temporal characteristics of extreme precipitation in Henan Province, this study applied RClimDex model to obtain nine extreme precipitation indices based on daily precipitation data from 90 meteorological stations from 1981 to 2020. Linear propensity estimation, M-K mutation test, Morlet wavelet analysis, and geostatistical analysis were used to investigate the spatial and temporal variation characteristics of the extreme precipitation indices in the region. The results indicated that continuous dry days (CDD), number of heavy rain days (R20mm), maximum daily precipitation (Rx1day), maximum precipitation for 5 consecutive days (Rx5day), and precipitation intensity (SDII) showed an overall increasing trend, but none passed the significance test (p > 0.01). Extremely strong precipitation (R99p) and Rx5day changed abruptly in 1994, and Rx1day and SDII changed abruptly in 2004. The seven extreme precipitation indices, except CDD and continuous wet days (CWD), had a 30-year cyclical pattern. The multi-year average of extreme precipitation indices showed that the CDD gradually decreased from north to south, CWD and R20mm gradually increased from north to south. Rx1day and Rx5day gradually increased from northwest to southeast, and SDII increased from west to east. The results can contribute valuable insights to extreme precipitation trends and future climate predictions in Henan Province and provide scientific support for coping with extreme precipitation changes and disaster prevention. [ABSTRACT FROM AUTHOR]

It is a consensus that Earth's climate has been warming. The impact of global warming is asymmetric, that is, there is more substantial warming in the daily minimum surface air temperature and lower warming in the maximum surface air temperature. Previous studies have reported diurnal temperature differences greatly affecting winter wheat yield. However, only a few studies have investigated the impact of global warming on the growth and yield of winter wheat, yet the influence of night warming on quality has not been deeply evaluated. In this study, two wheat cultivars were used as materials: Jimai 44 (JM44) with strong gluten and Jimai 22 (JM22) with medium gluten, to explore the effects of high nighttime temperatures (HNTs) on the growth, yield, and quality of wheat. The results show that HNTs significantly shortened seedling emergence and anthesis periods in both cultivars compared with ambient temperatures (ATs). In addition, HNTs increased the respiration rate at anthesis and grain-filling stages, impeding wheat pollination and grain maturity. HNTs also accelerated leaf senescence and increased the number of sterile spikelets and plant height, but decreased the effective tiller number, the number of spikes per unit area, and grains per spike. As a result, the grain yield of JM22 and JM44 was decreased by 24.6% and 21.2%, respectively. Moreover, HNTs negatively influenced the flour quality of the two wheat cultivars. The current findings provide new insights into the effects of HNTs on the growth, development, yield, and quality of different wheat genotypes during the whole growth period. [ABSTRACT FROM AUTHOR]

Anthropogenic emissions alter atmospheric composition and therefore the climate, with implications for air pollution- and climate-related human health. Mortality attributable to air pollution and non-optimal temperature is a major concern, expected to shift under future climate change and socioeconomic scenarios. In this work, results from numerical simulations are used to assess future changes in mortality attributable to long-term exposure to both non-optimal temperature and air pollution simultaneously. Here we show that under a realistic scenario, end-of-century mortality could quadruple from present-day values to around 30 (95% confidence level:12-53) million people/year. While pollution-related mortality is projected to increase five-fold, temperature-related mortality will experience a seven-fold rise, making it a more important health risk factor than air pollution for at least 20% of the world's population. These findings highlight the urgent need to implement stronger climate policies to prevent future loss of life, outweighing the benefits of air quality improvements alone. For different future scenarios, end-of-century mortality could quadruple from present day values. Importantly, the non-optimal temperature attributable mortality will increase at faster rate than the air pollution one, making it a more important health risk factor than air pollution for at least 20% of the world's population. [ABSTRACT FROM AUTHOR]

The understanding of recent climate extremes and the characterization of climate risk require examining these extremes within a historical context. However, the existing datasets of observed extremes generally exhibit spatial gaps and inaccuracies due to inadequate spatial extrapolation. This problem arises from traditional statistical methods used to account for the lack of measurements, particularly prevalent before the mid-20th century. In this work, we use artificial intelligence to reconstruct observations of European climate extremes (warm and cold days and nights) by leveraging Earth system model data from CMIP6 through transfer learning. Our method surpasses conventional statistical techniques and diffusion models, showcasing its ability to reconstruct past extreme events and reveal spatial trends across an extensive time span (1901-2018) that is not covered by most reanalysis datasets. Providing our dataset to the climate community will improve the characterization of climate extremes, resulting in better risk management and policies. The authors use artificial intelligence to accurately reconstruct past climate extremes from sparse observational data, providing quantitative evidence of hot and cold extremes in the early 20th century and shedding light on their evolution. [ABSTRACT FROM AUTHOR]

Compound events have become more frequent and diverse under global warming. This study specifically focuses on a type of compound events termed spatially compounding flood-nocturnal heat events over adjacent regions. Five flood hotspots are identified to compound with adjacent nocturnal heat. The flood and nocturnal heat are linked via a water vapor transport belt, with flood over the region of prominent water vapor convergence and ascending anomalies and nocturnal heat over the extension region with moderately increased humidity and weak vertical motion anomaly. The compound events for all the hotspots occur more frequently recently, with commonly positive contribution from the increasing trends of nocturnal temperature (TN) but various contribution from the trends of precipitation (Pr) and Pr-TN correlation. The positive contribution of enhanced Pr-TN correlation results from the enhanced variability of the circulation accompanied with water vapor transport. This study highlights the influence of atmospheric circulation variability on compound events. [ABSTRACT FROM AUTHOR]

Heatwaves (HWs) pose a severe threat to human and ecological systems. Here we assess the projected changes in heatwaves over Latin America using bias corrected high-resolution regional climate simulations under two Representative Concentration Pathway scenarios (RCPs). Heatwaves are projected to be more frequent, long-lasting, and intense in the mid-century under both RCP2.6 and RCP8.5 scenarios, with severe increases under the RCP8.5 scenario. Even under the low emissions scenario of RCP2.6, the frequency of heatwaves doubles over most of the region. A three- to tenfold rise in population exposure to heatwave days is projected over Central and South America, with climate change playing a dominant role in driving these changes. Results show that following the low emission pathway would reduce 57% and 50% of heatwave exposure for Central and South American regions respectively, highlighting the need to control anthropogenic emissions and implement sustainable practices. [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]

Canada‐wide, statistically downscaled simulations of global climate models from the Sixth Coupled Model Inter‐comparison Project (CMIP6) have been made available for 26 models using a new multivariate approach and an improved observational target dataset. These new downscaled scenarios comprise daily simulations of precipitation, maximum temperature, and minimum temperature at 1/12° resolution across Canada. Simulations from each of the 26 downscaled global climate models span a historical period (1950–2014), and three future Shared Socio‐economic Pathways (SSPs) representing low (SSP1 2.6), moderate (SSP2 4.5) and high (SSP5 8.5) future emissions from 2015 to 2100. Results from an evaluation of the multivariate downscaling method over Canada yield improved performance in replicating multivariate and compound climate indices compared to previously used univariate downscaling methods. This Multivariate Canadian Downscaled Climate Scenarios for CMIP6 (CanDCS‐M6) dataset is intended to facilitate climate impacts assessments, hydrologic modelling, and analysis tools for presenting climate projections. [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]

Anthropogenic climate change and associated increasing nutrient loading to coasts will worsen coastal eutrophication on a global scale. Basin Head is a coastal lagoon located in northeastern Prince Edward Island, Canada, with a federally protected ecosystem. Nitrate-nitrogen (NO3-N) is conveyed from agricultural fields in the watershed to the eutrophic lagoon via intertidal groundwater springs and groundwater-dominated tributaries. A field program focused on four main tributaries that discharge into the lagoon was conducted to measure year-round NO3-N loading. These measurements were used to calibrate a SWAT+ hydrologic model capable of simulating hydrologic and NO3-N loads to the lagoon. Several climate change scenarios incorporating different agricultural best management practices (BMPs) were simulated to better understand potential future NO3-N loading dynamics. Results indicate that all climate change scenarios produced increased annual NO3-N loading to the lagoon when comparing historical (1990-2020) to end of century time periods (2070-2100); however, only one climate scenario (MRI-ESM2-0 SSP5-8.5) resulted in a statistically significant (p-value <0.05) increase. Enlarged buffer strips and delayed tillage BMP simulations produced small (0%-8%) effects on loading, while changing the crop rotation from potato-barley-clover to potato-soybean-barley yielded a small reduction in NO3-N loading between the historical period and the end of the century (26%-33%). Modeling revealed changes in seasonal loading dynamics under climate change where NO3-N loads remained more consistent throughout the year as opposed to current conditions where the dominant load is in the spring. An increase in baseflow contributions to streamflow was also noted under climate change, with the largest change occurring in the winter (e.g., up to a five-fold increase in February). These findings have direct implications for coastal management in groundwater-dominated agricultural watersheds in a changing climate. [ABSTRACT FROM AUTHOR]

Analysing the impacts of climate change on water resources is crucial to identify vulnerabilities and prioritize actions. We investigated climate change impacts on drinking water supply, emphasizing sustainable agriculture as an adaptation strategy, using the SWAT model in the Cávado River basin, Portugal. Our study highlights an increase in months with river discharge below ecological flow post water abstraction due to climate change. Notably, nitrate concentration was more influenced by sustainable agriculture practices than climate change. Our study highlights the vital role of adaptive strategies, especially sustainable agriculture, in securing water resources amidst challenges posed by climate change. [ABSTRACT FROM AUTHOR]

Potential impacts of projected long-term climate change toward the end of the 21st century on rainfall and peak intensity of six super-typhoons in the western North Pacific (WNP) are assessed using a cloud-resolving model (CRM) and the pseudo-global warming (PGW) method, under two representative concentration pathway (RCP) emission scenarios of RCP4.5 and RCP8.5. Linear long-term trends in June–October are calculated from 38 Coupled Model Intercomparison Project phase 5 (CMIP5) models from 1981–2000 to 2081–2100, with warmings of about 3 °C in sea surface temperature, 4 °C in air temperature in the lower troposphere, and increases of 20% in moisture in RCP8.5. The changes in RCP4.5 are about half the amounts. For each typhoon, three experiments are carried out: a control run (CTL) using analysis data as initial and boundary conditions (IC/BCs), and two future runs with the trend added to the IC/BCs, one for RCP4.5 and the other for RCP8.5, respectively. Their results are compared for potential impacts of climate change. In future scenarios, all six typhoons produce more rain rather consistently, by around 10% in RCP4.5 and 20% in RCP8.5 inside 200–250 km from the center, with increased variability toward larger radii. Such increases are tested to be highly significant and can be largely explained by the increased moisture and water vapor convergence in future scenarios. However, using this method, the results on peak intensity are mixed and inconsistent, with the majority of cases becoming somewhat weaker in future runs. It is believed that in the procedure to determine the best initial time for CTL, which yielded the strongest TC, often within a few hPa in minimum central sea-level pressure to the best track data, an advantage was introduced to the CTL unintentionally. Once the long-term trends were added in future runs, the environment of the storm was altered and became not as favorable for subsequent intensification. Thus, the PGW approach may have some bias in assessing the peak intensity of such super-typhoon cases, and caution should be practiced. [ABSTRACT FROM AUTHOR]