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The field of extreme event attribution (EEA) has rapidly developed over the last two decades. Various methods have been developed and implemented, physical modelling capabilities have generally improved, the field of impact attribution has emerged, and assessments serve as a popular communication tool for conveying how climate change is influencing weather and climate events in the lived experience. However, a number of non-trivial challenges still remain that must be addressed by the community to secure further advancement of the field whilst ensuring scientific rigour and the appropriate use of attribution findings by stakeholders and associated applications. As part of a concept series commissioned by the World Climate Research Programme, this article discusses contemporary developments and challenges over six key domains relevant to EEA, and provides recommendations of where focus in the EEA field should be concentrated over the coming decade. These six domains are: (1) observations in the context of EEA; (2) extreme event definitions; (3) statistical methods; (4) physical modelling methods; (5) impact attribution; and (6) communication. Broadly, recommendations call for increased EEA assessments and capacity building, particularly for more vulnerable regions; contemporary guidelines for assessing the suitability of physical climate models; establishing best-practice methodologies for EEA on compound and record-shattering extremes; co-ordinated interdisciplinary engagement to develop scaffolding for impact attribution assessments and their suitability for use in broader applications; and increased and ongoing investment in EEA communication. To address these recommendations requires significant developments in multiple fields that either underpin (e.g., observations and monitoring; climate modelling) or are closely related to (e.g., compound and record-shattering events; climate impacts) EEA, as well as working consistently with experts outside of attribution and climate science more generally. However, if approached with investment, dedication, and coordination, tackling these challenges over the next decade will ensure robust EEA analysis, with tangible benefits to the broader global community. [ABSTRACT FROM AUTHOR]

Downscaling global climate models (GCMs) provides crucial high-resolution data needed for informed decision-making at regional scales. However, there is no uniform approach to select the most suitable GCMs. Over Southeast Asia (SEA), observations are sparse and have large uncertainties, complicating GCM selection especially for rainfall. To guide this selection, we apply a standardised benchmarking framework to select CMIP6 GCMs for dynamical downscaling over SEA, addressing current observational limitations. This framework identifies fit-for-purpose models through a two-step process: (a) selecting models that meet minimum performance requirements in simulating the fundamental characteristics of rainfall (e.g. bias, spatial pattern, annual cycle and trend) and (b) selecting models from (a) to further assess whether key precipitation drivers (monsoon) and teleconnections from modes of variability are captured, i.e. the El Niño–Southern Oscillation (ENSO) and Indian Ocean Dipole (IOD). GCMs generally exhibit wet biases, particularly over the complex terrain of the Maritime Continent. Evaluations from the first step identify 19 out of 32 GCMs that meet our minimum performance expectations in simulating rainfall. These models also consistently capture atmospheric circulations and teleconnections with modes of variability over the region but overestimate their strength. Ultimately, we identify eight GCMs meeting our performance expectations. There are obvious, high-performing GCMs from allied modelling groups, highlighting the dependency of the subset of models identified from the framework. Therefore, further tests of model independence, data availability and future climate change spread are conducted, resulting in a final subset of two independent models that align with our a priori expectations for downscaling over the Coordinated Regional Climate Downscaling Experiment –Southeast Asia (CORDEX-SEA). [ABSTRACT FROM AUTHOR]

In this study, we employ the Conformal Cubic Atmospheric Model (CCAM), a variable‐resolution global atmospheric model, driven by two distinct sea surface temperature (SST) data sets: the 0.25° Optimum Interpolation Sea Surface Temperature (CCAM_OISST) version 2.1 and the 2° Extended Reconstruction SSTs Version 5 (CCAM_ERSST5). Model performance is assessed using a benchmarking framework, revealing good agreement between both simulations and the climatological rainfall spatial pattern, seasonality, and annual trends obtained from the Australian Gridded Climate Data (AGCD). Notably, wet biases are identified in both simulations, with CCAM_OISST displaying a more pronounced bias. Furthermore, we have examined CCAM's ability to capture El Niño‐Southern Oscillation (ENSO) and Indian Ocean Dipole (IOD) correlations with rainfall during Austral spring (SON) utilizing a novel hit rate metric. Results indicate that only CCAM_OISST successfully replicates observed SON ENSO‐ and IOD‐rainfall correlations, achieving hit rates of 86.6% and 87.5%, respectively, compared to 52.7% and 41.8% for CCAM_ERSST5. Large SST differences are found surrounding the Australian coastline between OISST and ERSST5 (termed the "Coastal Effect"). Differences can be induced by the spatial interpolation error due to the discrepancy between model and driving SST. An additional CCAM experiment, employing OISST with SST masked by ERSST5 in 5° proximity to the Australian continent, underscores the "Coastal Effect" has a significant impact on IOD‐Australian rainfall simulations. In contrast, its influence on ENSO‐Australian rainfall is limited. Therefore, simulations of IOD‐Australian rainfall teleconnection are sensitive to local SST representation along coastlines, probably dependent on the spatial resolution of driving SST. Plain Language Summary: In this research, the Conformal Cubic Atmospheric Model (CCAM), a global atmospheric model, is used to study the impact of different driving sea surface temperature (SST) data sets on Australian rainfall simulations. Two SST data sets, one with high resolution (OISST) and another at lower resolution (ERSST5), are employed to drive CCAM (CCAM_OISST and CCAM_ERSST5). Model performance is evaluated using a benchmarking approach, indicating that both SST‐driven experiments are in good agreement with observed rainfall patterns in Australia. However, both simulations exhibit wet biases, with CCAM_OISST having a more noticeable bias. The study assesses CCAM's ability to capture the correlation between El Niño‐Southern Oscillation (ENSO) and Indian Ocean Dipole (IOD) with rainfall during Austral spring. Results reveal that CCAM_OISST performs better, replicating observed correlations more accurately than CCAM_ERSST5. The research identifies strong SST differences found between OISST and ERSST5 around the Australian coastline. An additional experiment underscores that this "Coastal Effect" plays an important role in simulating IOD‐Australian rainfall correlations, while its impact on ENSO‐Australian rainfall is limited. In conclusion, robust simulations of IOD‐Australian rainfall teleconnection require an accurate representation of local SST, which is related to the spatial resolution of SST products driving the model. Key Points: Novel hit rate metrics are proposed to evaluate El Niño‐Southern Oscillation and Indian Ocean Dipole‐rainfall teleconnectionsCCAM driven by different SST performs well in mean rainfall but ENSO and IOD related rainfall varies substantially over AustraliaResolution of the driving sea surface temperature is important to simulate IOD‐rainfall variability over Australia [ABSTRACT FROM AUTHOR]

Downscaling global climate models (GCMs) provides crucial, high-resolution data needed for informed decision-making at regional scales. However, there is no uniform approach to select the most suitable GCMs. Over Southeast Asia (SEA), observations are sparse and have large uncertainties, complicating GCM selection especially for rainfall. To guide this selection, we apply a standardised benchmarking framework to select CMIP6 GCMs for dynamical downscaling over SEA, addressing current observational limitations. This framework identifies fit-for-purpose models through a two-step process: (a) selecting models that meet minimum performance requirements in simulating the fundamental characteristics of rainfall (e.g., bias, spatial pattern, annual cycle, and trend) and (b) selecting models from (a) to further assess whether key precipitation drivers (monsoon) and teleconnections from modes of variability are captured [El Niño-Southern-Oscillation (ENSO) and Indian Ocean Dipole (IOD)]. GCMs generally exhibit wet biases, particularly over the complex terrain of the Maritime Continent. Evaluations from the first step identify 19 out of 32 GCMs that meet our minimum performance expectations in simulating rainfall. These models also consistently capture atmospheric circulations and teleconnections with modes of variability over the region but overestimate their strength. Ultimately, we identify eight GCMs meeting our performance expectations. There are obvious, high-performing GCMs from allied modelling groups, highlighting the dependency of the subset of models identified from the framework. Therefore, further tests on model independence, data availability, and future climate change spread are conducted, resulting in a final sub-set of two independent models that align with our a priori expectations for downscaling over CORDEX-SEA. [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]

The article titled "Corrigendum to 'Millions of digitized historical sea-level pressure observations rediscovered'" discusses a revision made to a dataset associated with a previous paper. The dataset includes over 5 million observations of sea level pressure from April 1919 to December 1960 over the UK & Ireland. The revision was prompted by the discovery that the column giving the pressure change over the previous 3 hours in the original documents had different units than previously assumed, requiring small revisions to the dataset. The article also mentions other details about the dataset and acknowledges the individual who brought the issue to their attention. [Extracted from the article]

The high latitude Southern Hemisphere (SH) is an important region for Earth's climate. Ocean heat content, cryosphere interactions, Antarctic bottom water development and the cloud‐albedo feedbacks need to be understood to form a complete picture of the climate system. However, the high latitude SH is one of the most under‐observed regions due to its remoteness. The advent of satellites and reanalyses have improved our monitoring of this region. Some previous studies observed an increase in precipitation over the SH high latitudes, however we argue that some of the trends in commonly used data sets may be artifacts. We use regression analysis of trends in precipitation and the Southern Annular Mode to contrast these relationships in satellite and reanalysis products, and to evaluate precipitation over the SH. We suggest that sensor changes and the lack of in situ data available for calibration may be responsible for unusual precipitation patterns especially around 65°S. Plain Language Summary: Many important processes in the Earth's climate occur at high latitudes in the Southern Hemisphere. However, due to its remoteness and inhospitable conditions, scientists have trouble obtaining data for this region. Satellites have helped to provide information about this area but are not well constrained due to a lack of in situ data. Sensor changes and the launch of new satellites means that the data can vary in quality and reliability over time. By comparing precipitation trends over the Southern Hemisphere with known climate patterns, we suggest that an observed increase in precipitation over the Southern Hemisphere high latitudes may be due to variations in the satellite technology rather than a physical increase in precipitation. This is important for understanding the impacts of climate change on Earth's water cycle and heat storage. Key Points: High latitude precipitation trends are likely artifactsSensor changes may be responsible for spurious trends in the Southern OceanSatellite products do not agree on the zonal mean precipitation pattern in the Southern Hemisphere [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]

Millions of sub‐daily sea‐level pressure observations taken between 1919 and 1960 over the British and Irish Isles were transcribed from paper records in the early 2000s but were not published and subsequently forgotten. A chance discussion led to the rediscovery of the transcribed data and 5.47 million observations from 160 locations are now made available, although the data have not been fully quality‐controlled. Much of the data are 3‐hourly, allowing for detailed examinations of synoptic weather variations for this region and time period, and will be invaluable for constraining future reanalyses. We illustrate the value of the data using a stormy period during October and November 1928 and discuss the remaining quality‐control issues. [ABSTRACT FROM AUTHOR]

Atmospheric warming results in an intensification of annual precipitation over the globe but large uncertainties remain regionally and at seasonal scales, especially for extremes. Using 29 models from the Sixth Phase of the Coupled Model Intercomparison Project (CMIP6), we investigate future seasonal changes in extreme precipitation (under the scenario SSP5‐8.5) and how it compares to changes in mean precipitation. Over land, we find a strong intensification of the wettest day in all seasons over the mid and high latitudes of the Northern Hemisphere and over India during the monsoon. Extreme intensity decreases in the subtropics for some seasons, including over large regions around the Mediterranean basin and Southern Africa, and these drying patterns are not apparent in annual results. The key finding is that the CMIP6 multi‐model mean always shows that seasonal changes in mean and extreme precipitation align where there is high model agreement. That is, in all seasons by the end of the 21st century, extremes intensify in regions where mean precipitation increases and decline where mean precipitation decreases. This should not hide inherent uncertainties associated, namely the large range of changes intensity that can be found across the models, and an important modulation of the changes by internal variability. Yet, this study shows that the multi‐model mean shows broad consistency such that future seasonal changes in mean precipitation could be used to infer future changes in extremes (and vice versa), thus providing valuable information for risk planning and mitigation strategies. Plain Language Summary: The rise in surface air temperature due to human influence results in an intensification of the most extreme events of precipitation over the globe. Although this is shown by both observations and Earth System Models at the global scale, more uncertainties remain regionally and at the seasonal scales. In this study, we use a large ensemble of state‐of‐the‐art Earth System Models and investigate future seasonal changes in extreme precipitation and how it compares to changes in total precipitation. Our key finding is that, in all seasons by the end of the 21st century, extremes intensify in regions where mean precipitation increases and decline where mean precipitation decreases. This is found locally and in regions where there is high agreement between the models with projected changes of a same sign. Hence, when future seasonal changes in precipitation can be assessed with high confidence, we find that changes in totals and extremes align in the future. Our results demonstrate that there is potential added value from examining concomitant projected changes in totals and extremes of precipitation on seasonal and regional scales which could help better inform decision‐making in relevant climate‐sensitive sectors. Key Points: We present projected seasonal changes in Coupled Model Intercomparison Project (CMIP6) total and extreme precipitation and show that they scale into the futureCMIP6 multi‐model mean seasonal change is of same sign for total and extreme precipitation when there is high agreement between modelsThis is found locally (at the grid cell scale) and regionally and is generally supported by the 29 individual models [ABSTRACT FROM AUTHOR]

The ability of regional climate models (RCMs) to accurately simulate the current climate is increasingly important for impact assessments over Southeast Asia (SEA), identified as one of the world's most vulnerable regions to climate change. In this study, we evaluate the performance of a set of regional high‐resolution simulations from the Coordinated Regional Climate Downscaling Experiment‐SEA (CORDEX‐SEA) in simulating rainfall over the region. Simulations of the 1982–2005 seasonal mean climatology of daily precipitation and precipitation distribution over land are compared to observations from different sources (i.e., in situ‐based and satellite‐based). We also evaluate to what extent the precipitation distribution in RCMs is closer to observations than their associated forcing global climate models (GCMs). Observational estimates of precipitation over SEA have large uncertainties, making the model evaluations complicated. Despite these difficulties, our results highlight that RCMs can reproduce some complexities in the spatial distribution of seasonal rainfall but generally have a larger wet bias than GCMs. This is particularly true for the extremes in which RCMs show a large overestimation of rainfall intensity. There are some precipitation quantiles and grid points in which RCMs show limited reductions in biases compared to observations, but there is no consistency across all simulations and RCMs are generally further away from observations than their forcing GCMs. We find that greater intensity in RCMs over CORDEX‐SEA compared to their associated forcing GCMs is firstly associated with the increased supply of moisture from both local and large‐scale sources. Second, a widespread increase in convective precipitation is found across the region in RCMs. Our findings suggest that a model's ability to simulate precipitation over the region relies more on the RCM setup itself (e.g., parameterization scheme), rather than its forcing GCM. This should be considered when assessing the reliability of RCM precipitation simulations for future projections. [ABSTRACT FROM AUTHOR]

This study focuses on the projections and time of emergence (TOE) for temperature extremes over Australian regions in the phase 6 of Coupled Model Intercomparison Project (CMIP6) models. The model outputs are based on the Shared Socioeconomic Pathways (SSPs) from the Tier 1 experiments (i.e., SSP1‐2.6, SSP2‐4.5, SSP3‐7.0, and SSP5‐8.5) in the Scenario Model Intercomparison Project (ScenarioMIP), which is compared with the Representative Concentration Pathways (RCPs) in CMIP5 (i.e., RCP2.6, RCP4.5, and RCP8.5). Furthermore, two large ensembles (LEs) in CMIP6 are used to investigate the effects of internal variability on the projected changes and TOE. As shown in the temporal evolution and spatial distribution, the strongest warming levels are projected under the highest future scenario and the changes for some extremes follow a "warm‐get‐warmer" pattern over Australia. Over subregions, tropical Australia usually shows the highest warming. Compared to the RCPs in CMIP5, the multi‐model medians in SSPs are higher for some indices and commonly exhibit wider spreads, likely related to the different forcings and higher climate sensitivity in a subset of the CMIP6 models. Based on a signal‐to‐noise framework, we confirm that the emergence patterns differ greatly for different extreme indices and the large uncertainty in TOE can result from the inter‐model ranges of both signal and noise, for which internal variability contributes to the determination of the signal. We further demonstrate that the internally generated variations influence the noise. Our findings can provide useful information for mitigation strategies and adaptation planning over Australia. Key Points: A "warm‐get‐warmer" pattern exists for some extremes over Australia and tropical regions usually show the highest warmingCompared to CMIP5, the higher warming for some extremes in CMIP6 can lead to earlier time of emergenceInternal variability influences the determination of the noise [ABSTRACT FROM AUTHOR]

Extreme precipitation occurring on consecutive days may substantially increase the risk of related impacts, but changes in such events have not been studied at a global scale. Here we use a unique global dataset based on in situ observations and multimodel historical and future simulations to analyze the changes in the frequency of extreme precipitation on consecutive days (EPCD). We further disentangle the relative contributions of variations in precipitation intensity and temporal correlation of extreme precipitation to understand the processes that drive the changes in EPCD. Observations and climate model simulations show that the frequency of EPCD is increasing in most land regions, in particular, in North America, Europe, and the Northern Hemisphere high latitudes. These increases are primarily a consequence of increasing precipitation intensity, but changes in the temporal correlation of extreme precipitation regionally amplify or reduce the effects of intensity changes. Changes are larger in simulations with a stronger warming signal, suggesting that further increases in EPCD are expected for the future under continued climate warming. [ABSTRACT FROM AUTHOR]

Marine cold‐spell (MCS) metrics—such as frequency and intensity—are decreasing globally, while marine heatwave (MHW) metrics are increasing due to sea surface temperature (SST) warming. However, the concomitant changes in MHW and MCS metrics, and whether SST warming can similarly explain the decreasing MCS metrics remain unclear. Here, we provide a comparative global assessment of these changes based on satellite SST observations over 1982–2020. Across the globe, we find distinct differences in mean MHW and MCS metrics. Furthermore, decreasing trends in MCS metrics are not necessarily aligned with increasing trends in MHW metrics. While differences in intensity trends are mainly explained by SST variance trends, differences in trends of annual days are less clear. Overall, decreasing MCS days and intensities are found to be largely driven by warming SST, rather than SST variance changes. Therefore, it is expected that MCS days and intensity will continue diminishing under global warming. Plain Language Summary: Persistent ocean temperature extremes—known as marine heatwaves (MHWs) and marine cold‐spells (MCSs) —can cause severe impacts on marine ecosystems and fisheries. MHWs have been shown to be increasing in frequency and intensity over the last decades, which is largely explained by the mean warming of sea surface temperature (SST). Although MCSs have decreased in frequency and have weakened globally, we examine whether long‐term SST warming can similarly explain the changes in MCSs. To address this, we characterize MCSs and MHWs using 39 years (1982–2020) of SST satellite observations. We find that the changes in annual MHW days and MHW intensity are not entirely commensurate with changes in annual MCS days and MCS intensity. Our findings imply that there is a non‐uniform shift in temperature extremes, and therefore in SST variations. Statistical tests on the decrease in annual MCS days and MCS intensity, however, suggest that these can be largely explained by mean warming. Key Points: Marine cold‐spell (MCS) intensity and annual MCS days (the number of MCS days in each year) are decreasing across most global oceansThe decreasing MCS annual days and intensity can be largely explained by sea surface temperature warming, in line with global warmingThe differences between MCS and marine heatwave intensity trends can be largely explained by changes in sea surface temperature variance [ABSTRACT FROM AUTHOR]

Australia experiences some of the world's most variable rainfall. Previous studies have mostly focused on understanding rainfall variability in terms of frequency and intensity. However, understanding the timing of when extreme rainfall occurs is crucial for seasonal prediction, although it largely remains unexplored. Here we investigate the timing of extreme rainfall in Australia and the spatial variability of this timing. This study examines how some of the large‐scale drivers, such as the El Niño–Southern Oscillation (ENSO) and the Interdecadal Pacific Oscillation (IPO), determine the timing and interannual variability of the timing of extreme rainfall in Australia. Our results show that there is a clear spatial north–south delineation in the season when extreme rainfall occurs in Australia, shown by a contour diagonally extending roughly from 21°S in the west of Australia to 33°S in the east. North of this contour, extreme rainfall usually occurs in austral summer, with the smallest interannual variability in the timing of extreme rainfall in this region. In the south, extreme rainfall usually occurs in autumn/winter months; however, the timing is highly variable. In southeast Australia (SEA), extreme rainfall can fall at any time of the year, which makes seasonal prediction extremely challenging for this region. Both observation and reanalysis data show that the area where extreme rainfall occurs in summer extends further south during negative IPO years. We also find that IPO and ENSO phases, and the interaction between them, play significant roles in both determining the timing of extreme rainfall and constraining the interannual variability, especially in SEA. We focus on SEA for further analysis as this region shows the greatest shift in seasonality of extremes in response to large‐scale variability. We conclude that studying the relationship between rainfall and large‐scale drivers is important for verification and improvement of the seasonal prediction of extreme rainfall. [ABSTRACT FROM AUTHOR]

Estimates of observed long-term changes in daily precipitation globally have been limited due to availability of high-quality observations. In this study, a new gridded dataset of daily precipitation, called Rainfall Estimates on a Gridded Network (REGEN) V1–2019, was used to perform an assessment of the climatic changes in precipitation at each global land location (except Antarctica). This study investigates changes in the number of wet days (≥1 mm) and the entire distribution of daily wet- and all-day records, in addition to trends in annual and seasonal totals from daily records, between 1950 and 2016. The main finding of this study is that precipitation has intensified across a majority of land areas globally throughout the wet-day distribution. This means that when it rains, light, moderate, or heavy wet-day precipitation has become more intense across most of the globe. Widespread increases in the frequency of wet days are observed across Asia and the United States, and widespread increases in the precipitation intensity are observed across Europe and Australia. Based on a comparison of spatial pattern of changes in frequency, intensity, and the distribution of daily totals, we propose that changes in light and moderate precipitation are characterized by changes in precipitation frequency, whereas changes in extreme precipitation are primarily characterized by intensity changes. Based on the uncertainty estimates from REGEN, this study highlights all results in the context of grids with high-quality observations. [ABSTRACT FROM AUTHOR]

This paper provides an updated analysis of observed changes in extreme precipitation using high-quality station data up to 2018. We examine changes in extreme precipitation represented by annual maxima of 1-day (Rx1day) and 5-day (Rx5day) precipitation accumulations at different spatial scales and attempt to address whether the signal in extreme precipitation has strengthened with several years of additional observations. Extreme precipitation has increased at about two-thirds of stations and the percentage of stations with significantly increasing trends is significantly larger than that can be expected by chance for the globe, continents including Asia, Europe, and North America, and regions including central North America, eastern North America, northern Central America, northern Europe, the Russian Far East, eastern central Asia, and East Asia. The percentage of stations with significantly decreasing trends is not different from that expected by chance. Fitting extreme precipitation to generalized extreme value distributions with global mean surface temperature (GMST) as a covariate reaffirms the statistically significant connections between extreme precipitation and temperature. The global median sensitivity, percentage change in extreme precipitation per 1 K increase in GMST is 6.6% (5.1% to 8.2%; 5%–95% confidence interval) for Rx1day and is slightly smaller at 5.7% (5.0% to 8.0%) for Rx5day. The comparison of results based on observations ending in 2018 with those from data ending in 2000–09 shows a consistent median rate of increase, but a larger percentage of stations with statistically significant increasing trends, indicating an increase in the detectability of extreme precipitation intensification, likely due to the use of longer records. [ABSTRACT FROM AUTHOR]

We present the second update to a data set of gridded land‐based temperature and precipitation extremes indices: HadEX3. This consists of 17 temperature and 12 precipitation indices derived from daily, in situ observations and recommended by the World Meteorological Organization (WMO) Expert Team on Climate Change Detection and Indices (ETCCDI). These indices have been calculated at around 7,000 locations for temperature and 17,000 for precipitation. The annual (and monthly) indices have been interpolated on a 1.875°×1.25° longitude‐latitude grid, covering 1901–2018. We show changes in these indices by examining "global"‐average time series in comparison with previous observational data sets and also estimating the uncertainty resulting from the nonuniform distribution of meteorological stations. Both the short and long time scale behavior of HadEX3 agrees well with existing products. Changes in the temperature indices are widespread and consistent with global‐scale warming. The extremes related to daily minimum temperatures are changing faster than the maximum. Spatial changes in the linear trends of precipitation indices over 1950–2018 are less spatially coherent than those for temperature indices. Globally, there are more heavy precipitation events that are also more intense and contribute a greater fraction to the total. Some of the indices use a reference period for calculating exceedance thresholds. We present a comparison between using 1961–1990 and 1981–2010. The differences between the time series of the temperature indices observed over longer time scales are shown to be the result of the interaction of the reference period with a warming climate. The gridded netCDF files and, where possible, underlying station indices are available from www.metoffice.gov.uk/hadobs/hadex3 and www.climdex.org. Key Points: We present an updated data set of gridded temperature and precipitation extremesSpatio‐temporal coverage over 1901–2018 for 29 extremes indices was improved over previous versionWe find increased (decreased) intensity and frequency of warm (cool) extremes and increased intensity of heavy precipitation events [ABSTRACT FROM AUTHOR]

Finer grids in global climate models could lead to an improvement in the simulation of precipitation extremes. We assess the influence on model performance of increasing spatial resolution by evaluating pairs of high‐ and low‐resolution forced atmospheric simulations from six global climate models (generally the latest CMIP6 version) on a common 1° × 1° grid. The differences in tuning between the lower and higher resolution versions are as limited as possible, which allows the influence of higher resolution to be assessed exclusively. We focus on the 1985–2014 climatology of annual extremes of daily precipitation over global land, and models are compared to observations from different sources (i.e., in situ‐based and satellite‐based) to enable consideration of observational uncertainty. Finally, we address regional features of model performance based on four indices characterizing different aspects of precipitation extremes. Our analysis highlights good agreement between models that precipitation extremes are more intense at higher resolution. We find that the spread among observations is substantial and can be as large as intermodel differences, which makes the quantitative evaluation of model performance difficult. However, consistently across the four precipitation extremes indices that we investigate, models often show lower skill at higher resolution compared to their corresponding lower resolution version. Our findings suggest that increasing spatial resolution alone is not sufficient to obtain a systematic improvement in the simulation of precipitation extremes, and other improvements (e.g., physics and tuning) may be required. Key Points: Models generally agree on an intensification of precipitation extremes at higher spatial atmospheric resolutionObservational uncertainties are substantial for precipitation extremes, which makes the evaluation of the models difficultIncreasing spatial resolution alone is not sufficient to obtain a systematic improvement in the simulation of precipitation extremes [ABSTRACT FROM AUTHOR]