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Abstract: Precipitation and temperature extremes from daily data indexed using the CLIMDEX methodology were calculated over the Central American region. The data comprises the coarsened versions of the Climate Hazards and Infrared Precipitation with stations (CHIRPs) and the corresponding data set for temperature (CHIRTs) from the year 1981 to 2020 and 1983 to 2016, respectively. The objective is to detect trend patterns in extremes in recent periods, use novel statistical techniques for assessing the trend significance and study the monthly and annual trends for each of the indices. Trends of extreme temperature indices show more consistent, robust and widespread significant results according with the observed warming of the region. Significant extreme precipitation indices trends are more localized, and therefore harder to analyse, but it seems that one robust result from several indices is the trend toward more intense extreme precipitation events in Costa Rica. The findings of this work suggest possible impacts in human and environmental systems across the region.

We offer an alternative proof, using the Stein-Chen method, of Bollobás' theorem concerning the distribution of the extreme degrees of a random graph. Our proof also provides a rate of convergence of the extreme degree to its asymptotic distribution. The same method also applies in a more general setting where the probability of every pair of vertices being connected by edges depends on the number of vertices. [ABSTRACT FROM AUTHOR]

The 2021 heatwave over Western North America (WNA) led to record‐breaking air temperatures and human‐perceived heat stress (humidex) values. The event was accompanied by drier conditions driven by prolonged atmospheric blocking. During the heatwave, the maximum 6‐day means of humidex and temperature (HX‐6 and TX‐6) exhibited larger anomalies (6.70 and 5.57°C) compared to the 95th percentiles (HX95 and TX95) (4.12 and 3.73°C), relative to 1981–2021 extended summer (June‐September) averages. Extreme indices of humidex show faster and larger increases than those of temperature, reflecting the nonlinear positive relationship between humidex and temperature. Future projections from a multi‐model ensemble of 19 Coupled Model Intercomparison Project Phase six (CMIP6) Global Climate Models (GCMs) clearly show an increase in humidex and temperature extremes, especially under intermediate and high emissions scenarios. Humidex indices (HX‐6 and HX95) show faster and larger increases than temperature indices (TX‐6 and TX95) for the same future years and global warming levels. Controlling for differences in GCM climate sensitivity to greenhouse gas forcing yields robust projections at various global warming levels, reducing the ranges of projected changes from the multi‐model ensemble. At 3.0°C global warming from pre‐industrial, the multi‐model ensemble projects occurrences of HX‐6, TX‐6, HX95, and TX95 over WNA that exceed 2021 levels to occur every 3.9, 1.7, 1.4, and 2.2 years, respectively, increasing to almost annually at 4.0°C. Plain Language Summary: In 2021 summer, Western North America (WNA) experienced an unprecedented heatwave, breaking air temperature and human‐perceived heat stress (humidex) records despite lower humidity due to prolonged atmospheric blocking. During the heatwave, consecutive 6‐day means of heat stress (HX‐6) and temperature (TX‐6) showed larger anomalies (6.70 and 5.57°C) compared to the 95th percentiles (HX95 and TX95) (4.12 and 3.73°C) over the region, relative to their averages over the 1981–2010 summer period (June‐September). Heat stress increased faster and more significantly than air temperature due to their nonlinear relationship. Future projections from 19 climate models reveal clear increases in heat stress and temperature, especially under intermediate and high emission scenarios. Heat stress indices show larger and faster increases than temperature indices at the same future years and global warming levels. Accounting for uncertainty related to climate model sensitivity enhances projection reliability. At 3.0°C global warming from pre‐industrial levels, 2021‐level events over WNA are projected to occur every few years, becoming an almost annual occurrence at 4.0°C. The study underscores the critical need for robust projections using multi‐model ensembles. Key Points: Unprecedented 2021 summer heatwave over Western North America resulted in record‐breaking humidex and air temperature extremesLarger increases in humidex than temperature, exceeding their 2021 levels, under intermediate and high emissions scenariosRobust projections of multi‐model ensemble, mitigating climate sensitivity uncertainty of GCMs response to greenhouse gas emissions [ABSTRACT FROM AUTHOR]

North Atlantic midlatitude cyclones are among the most severe weather systems, causing enormous economic damages and threatening human lives. The cyclone is typically characterized by cyclonic convergent surface winds, strong updrafts, and precipitation. However, extreme surface winds are often observed within the cyclone where downdrafts develop. The present study investigates the dynamical and thermodynamical characteristics of the horizontal winds impinging on the cold frontal surface and the associated downdrafts. It is shown that the cyclonic winds into the cold frontal surface are mainly responsible for the downdrafts that transport the high‐altitude horizontal momentum to the surface and cause intense surface winds. About half of the North Atlantic midlatitude cyclones are accompanied by the downdrafts especially in the southern and western parts of the cyclone center. Plain Language Summary: In the midlatitudes, air temperature decreases toward the poles, and cyclone systems typically travel eastward along regions characterized by strong meridional temperature gradients. Over the cyclone‐influencing area, the cyclonic, counterclockwise rotating, wind induces southward cold advection on the western side of the cyclone center. Therefore, during the early phase of the North Atlantic midlatitude cyclone, the generation of the cold front, which has a steep gradient of temperature to the west of the cyclone center, and that is a common feature. Even after the formation of the cold front, the horizontal winds keep impinging on the frontal surface. Then the horizontal winds blocked by the frontal surface and downdrafts are induced. The downdrafts transport the upper‐level intense wind speed to the lower level strengthening the surface winds. Key Points: Surface winds within North Atlantic midlatitude cyclones are strengthened by strong downdrafts and downward momentum transportsDowndrafts can be generated by the horizontal winds impinging on the cold frontal surfaceCyclones tend to accompany the downdrafts more to the south and west of the cyclone center [ABSTRACT FROM AUTHOR]

Three ~12‐km reanalysis‐driven regional climate models (RCMs) are evaluated in terms of capturing climatologies and extremes of precipitation, temperature and surface wind over Aotearoa/New Zealand (NZ). NZ provides an excellent case study for evaluating high‐resolution RCMs due to its coastal and complex terrain and isolated geographical position in the midlatitudes, exposed to both tropical and polar influences. Overall, we find that the RCMs faithfully reproduce the observed climate, with precipitation and temperature climatologies particularly well‐captured. However, the RCMs display significant differences from observations in capturing the surface wind climatology, highlighting a remaining key challenge. The excess “drizzle problem” is apparent to varying degrees, leading to a weaker representation in the length of meteorological drought in some regions. The RCMs also diverge in reproducing diurnal temperature range which appears partly related to cloud cover. Finally, we discuss the important role of observational uncertainty in the context of model evaluation. [ABSTRACT FROM AUTHOR]

More than 1000 years, the Meiyu–Baiu have shaped the uniqueness of natural resources, civilization and culture in the Yangtze River Basin of China and the main islands of Japan. In recent decades, frequent rainstorms and droughts have seemingly diminished the misty features of traditional Meiyu–Baiu rainfall. However, there is still no consensus on whether their traditional nature is suspended. In this study, we quantitatively demonstrate that the Meiyu–Baiu almost completely lost their traditional features during 1961–2023, ∼80% of which can be attributed to anthropogenic warming. Furthermore, in a warmer future, the traditional Meiyu–Baiu will be more unlikely to appear. This study underscores the urgency in adapting to climate shift because destructive extremes are measurably taking the place of mild and maternal rains. [ABSTRACT FROM AUTHOR]

Antarctic coastal surface winds affect ice‐sheet stability, sea ice, and local ecosystems. The strongest coastal winds are especially important due to the nonlinear relationship between wind speed and wind stress. We investigate the dynamics of extreme coastal winds using a simplified momentum budget calculated across the period 2010–2020 from the ERA5 reanalysis. The pressure‐gradient forcing term in the budget is decomposed into a large‐scale component and one associated with the temperature deficit layer. The role of budget terms across the coastal sector is compared for weak and strong winds. We then calculate composites of the top 100 easterly wind events across six east Antarctic coastal sectors, identifying terms responsible for the evolution of coastal extremes. A simple balance of terms exists offshore, dominated by large‐scale forcing, contrasting with the complex balance in the onshore sector where katabatic forcing is large. Large‐scale forcing explains 57% of offshore coastal wind‐speed variance overall, improving to 81% when budget terms associated with the temperature deficit layer and horizontal advection are included, with significant regional variation. The residual term plays an increasingly active role as wind speed increases. Extremes in all coastal sectors are associated with a synoptic‐scale transient dipole of pressure anomalies driving warm‐air advection. Although katabatic forcing is a very large term in magnitude, it is found to play a passive role, declining as wind speeds increase during extreme conditions. In some regions, an anomalous southerly component develops during extremes, which we attribute to an ageostrophic barrier wind. This research underscores the major role for large‐scale forcing in Antarctica's coastal winds, but also reveals a significant regional locally driven component. The results have implications for improving numerical model simulations of coastal easterlies and for studying their impacts on ocean circulation, sea ice, and ice‐shelf basal melt. [ABSTRACT FROM AUTHOR]

The 1997 New Year's flood event was the most costly in California's history. This compound extreme event was driven by a category 5 atmospheric river that led to widespread snowmelt. Extreme precipitation, snowmelt, and saturated soils produced heavy runoff causing widespread inundation in the Sacramento Valley. This study recreates the 1997 flood using the Regionally Refined Mesh capabilities of the Energy Exascale Earth System Model (RRM-E3SM) under prescribed ocean conditions. Understanding the processes causing extreme events informs practical efforts to anticipate and prepare for such events in the future, and also provides a rich context to evaluate model skill in representing extremes. Three California-focused RRM grids, with horizontal resolution refinement of 14 km down to 3.5 km, and six forecast lead times, 28 December 1996 at 00Z through 30 December 1996 at 12Z, are assessed for their ability to recreate the 1997 flood. Planetary to synoptic scale atmospheric circulations and integrated vapor transport are weakly influenced by horizontal resolution refinement over California. Topography and mesoscale circulations, such as the Sierra barrier jet, are better represented at finer horizontal resolutions resulting in better estimates of storm total precipitation and storm duration snowpack changes. Traditional time-series and causal analysis frameworks are used to examine runoff sensitivities state-wide and above major reservoirs. These frameworks show that horizontal resolution plays a more prominent role in shaping reservoir inflows, namely the magnitude and time-series shape, than forecast lead time, 2-to-4 days prior to the 1997 flood onset.

Climate change is currently one of the most important challenges faced by mankind. The purpose of this article is to give a coherent account of the scope and nature of these changes and their impact on universal security threats. The research process consisted largely of a review of the literature on the subject and an analysis of specialized, available documents and reports prepared by international environmental organizations. Climate change includes increases in temperature, changes in precipitation, sea level rise, extreme weather events and many other impacts that have enormous consequences for people, ecosystems and economies. Barriers are described that could prove to be an obstacle to implementing more sustainable development that could directly contribute to increased preventive and adaptive capacity, reduced emissions and vulnerability. The importance of adaptive capacity, which is a complex function of wealth, technology, information, skills, infrastructure, institutions and equity, was emphasized.

Accurate modeling of snow depth (SD) processes is critical for understanding global energy balance changes, affecting climate change mitigation strategies. This study evaluates the Coupled Model Intercomparison Project Phase 6 (CMIP6) model performance in simulating daily SD across Canada. We assess CMIP6 outputs against observed data, focusing on daily SD averages, snow cover durations, and rates of accumulation and depletion, alongside annual SD peaks for 11 major Canadian catchments. Our findings reveal that CMIP6 simulations generally overestimate daily SD by 57.7% and extend snow cover duration by 30.5 days on average. While three models (CESM2, UKESM1‐0‐LL and MIROC6) notably align with observed annual SD peaks, simulation biases suggest the need for enhanced model parameterization to accurately capture snow physics, particularly in regions with permanent snow cover and complex terrains. This analysis underscores the necessity of refining CMIP6 simulations and incorporating detailed geographical data for better SD predictions. Plain Language Summary: Precise simulations of snow depth (SD) are crucial for understanding changes in the Earth's energy balance and helping efforts to combat climate change. In this research, we evaluate how well the latest phase of the Coupled Model Intercomparison Project (CMIP6) can simulate the daily SD across Canada. By comparing these simulations to various SD data sets, we looked at average daily SD, how long snow remains on the ground, and how quickly it accumulates or melts away, focusing on 11 major catchments in Canada. Our study found that the CMIP6 models tend to simulate more SD, by about 57.7% on average, and suggest that snow cover remains around 30.5 days longer than observed durations. Although three specific models excel in matching the annual peak of daily SD, we noticed biases that point to a need for improving how these models represent various land covers. Our findings highlight the importance of making these models more accurate by using more detailed information about land cover properties, which helps better predict SD and understand its role in climate change. Key Points: Coupled model intercomparison project phase 6 (CMIP6) simulations mostly overestimate the average daily SD values by a median of 57.7% (6.9 cm)The average snow cover duration is overestimated by 30.5 days compared to station data, confirmed by the CMC data setCMIP6 simulations relatively capture extremes well, with a median bias of 12.73% [ABSTRACT FROM AUTHOR]

Quantifying the risk from extreme weather events in a changing climate is essential for developing effective adaptation and mitigation strategies. Climate models capturing different scenarios are often the starting point for physical risk. However, accurate risk assessment for mitigation and adaptation often demands a level of detail they typically cannot resolve. Here, we develop a dynamic data‐driven downscaling (super‐resolution) method that incorporates physics and statistics in a generative framework to learn the fine‐scale spatial details of rainfall. Our approach transforms coarse‐resolution (0.25°) climate model outputs into high‐resolution (0.01°) rainfall fields while efficaciously quantifying the hazard and its uncertainty. The downscaled rainfall fields closely match observed spatial fields and their distributions. Contrary to conventional thinking, our results suggest that coupling simple statistics and physics to learning improves the efficacy of downscaling midlatitude rainfall extremes from climate models. Plain Language Summary: Accurate risk assessment of extreme weather events in a changing climate requires high‐resolution data, which is expensive for climate models to produce. Thus, there arises a need for downscaling (super‐resolution) methods. We show that by using simplified physics and statistics to prime generative adversarial learning, one can reconstruct observed high‐resolution mid‐latitude extreme rainfall fields from low‐resolution climate models with high fidelity. As a result, uncertainty quantifying downscaling (25:1 ratio) has become feasible, overcoming limitations of extant dynamical and statistical downscaling methods. Key Points: We leverage data, physics, and machine learning to develop an approach to downscale rainfallPriming a two‐step generative adversarial learning with simplified physics and statistics and applying optimal estimation‐based bias correction overcomes common limitations of conventional downscaling strategiesThe proposed downscaling method enables the rapid and robust estimation of extreme rainfall hazards [ABSTRACT FROM AUTHOR]

We consider the clustering of extremes for stationary regularly varying random fields over arbitrary growing index sets. We study sufficient assumptions on the index set such that the limit of the point processes of the exceedances above a high threshold exists. Under the so-called anti-clustering condition, the extremal dependence is only local. Thus the index set can have a general form compared to previous literature (Basrak and Planinić in Bernoulli 27(2):1371–1408, 2021; Stehr and Rønn-Nielsen in Extremes 24(4):753–795, 2021). However, we cannot describe the clustering of extreme values in terms of the usual spectral tail measure (Wu and Samorodnitsky in Stochastic Process Appl 130(7):4470–4492, 2020) except for hyperrectangles or index sets in the lattice case. Using the recent extension of the spectral measure for star-shaped equipped space (Segers et al. in Extremes 20:539–566, 2017), the Υ -spectral tail measure provides a natural extension that describes the clustering effect in full generality. [ABSTRACT FROM AUTHOR]

Graphical models with heavy-tailed factors can be used to model extremal dependence or causality between extreme events. In a Bayesian network, variables are recursively defined in terms of their parents according to a directed acyclic graph (DAG). We focus on max-linear graphical models with respect to a special type of graph, which we call a tree of transitive tournaments. The latter is a block graph combining in a tree-like structure a finite number of transitive tournaments, each of which is a DAG in which every two nodes are connected. We study the limit of the joint tails of the max-linear model conditionally on the event that a given variable exceeds a high threshold. Under a suitable condition, the limiting distribution involves the factorization into independent increments along the shortest trail between two variables, thereby imitating the behaviour of a Markov random field. We are also interested in the identifiability of the model parameters in the case when some variables are latent and only a subvector is observed. It turns out that the parameters are identifiable under a criterion on the nodes carrying the latent variables which is easy and quick to check. [ABSTRACT FROM AUTHOR]

Australia is one of the regions strongly affected by the El Niño‐Southern Oscillation (ENSO). The recent 2020–2023 La Niña event was marked by record‐breaking rainfall and flooding across eastern Australia. The continuous wet conditions during the triple La Niña motivated us to explore the impacts of single‐year and multi‐year ENSO events on Australian rainfall using observational data sets. We find that, while there is no difference in the rainfall impacts during single or double El Niño events, Australian rainfall tends to increase in the third year of triple La Niña events compared to the first and second years. The enhanced rainfall impact during the third La Niña year occurs despite no strengthening of La Niña in the tropical Pacific, suggesting that other processes such as local rainfall‐soil moisture feedback may play a role in prolonging the effects of multi‐year La Niña events in Australia. Plain Language Summary: Australia is strongly affected by the El Niño‐Southern Oscillation (ENSO), with rainfall more likely to increase during La Niña and below‐average rainfall more common during El Niño. The recent 2020–2023 multi‐year La Niña was marked by continuous wet conditions across eastern Australia, leading to record‐breaking rainfall and flooding. Multi‐year La Niña events, where La Niña occurs in two or three consecutive austral summers, happened in about 50% of all La Niña events, including five triple La Niña events since 1900. We explored the impacts of multi‐year ENSO events on Australian rainfall and found that, while there is no difference in the rainfall impacts during single or double El Niño events, rainfall tends to increase in the third year of triple La Niña events compared to the first and second years. This rainfall increase occurs despite no strengthening of La Niña in the tropical Pacific Ocean, suggesting that local processes such as feedback between high/saturated soil moisture and rainfall may play a role in prolonging the effects of multi‐year La Niña events in Australia. Key Points: Eastern Australia tends to experience record‐breaking rainfall and flooding during La Niña eventsRainfall impact of multi‐year El Niño‐Southern Oscillation (ENSO) persists during double and triple events, despite no strengthening of ENSOAustralian rainfall increases in the third year of triple La Niña likely due to soil moisture‐rainfall feedback [ABSTRACT FROM AUTHOR]

Water and disaster risk management require accurate information about hydrometeorological extremes. However, estimation of rare events using extreme value analysis is hampered by short observational records, with large resulting uncertainties. Here, we present a surrogate world setup that makes use of data samples from meteorological and hydrological seasonal re‐forecasts to explore extremes for long return periods. The surrogate timeseries allow us to pool the re‐forecasts into 1000‐year‐long timeseries. We can then calculate return values of extremes and explore how they are affected by the size of sub‐samples as method for estimating the uncertainty. The approach relies on the fact that probabilistic seasonal re‐forecasts, initialized with perturbed initial conditions, have limited predictive skill with increasing lead time. At long lead times re‐forecasts will diverge into independent samples. The meteorological seasonal re‐forecasts are taken from the SEAS5 system, and hydrological re‐forecasts are generated with the E‐HYPE process‐based model for the pan‐European domain. Extreme value analysis is applied to annual maxima of precipitation and streamflow for return periods of 100 years. The analysis clearly demonstrates the large uncertainty in long return period estimates with typical available samples of only few decades. The uncertainty is somewhat reduced for 100‐year samples, but several 100 years seem to be necessary to have robust estimates. The bootstrap with replacement approach is applied to shorter timeseries, and is shown to well reproduce the uncertainty range of the longer samples. However, the main estimate of the return value can be significantly offset. Although the method is model based, with the associated uncertainties and bias compared to the real world, the surrogate approach is likely useful to explore rare and compounding extremes. [ABSTRACT FROM AUTHOR]

We present a variable‐resolution global chemistry‐climate model (AM4VR) developed at NOAA's Geophysical Fluid Dynamics Laboratory (GFDL) for research at the nexus of US climate and air quality extremes. AM4VR has a horizontal resolution of 13 km over the US, allowing it to resolve urban‐to‐rural chemical regimes, mesoscale convective systems, and land‐surface heterogeneity. With the resolution gradually reducing to 100 km over the Indian Ocean, we achieve multi‐decadal simulations driven by observed sea surface temperatures at 50% of the computational cost for a 25‐km uniform‐resolution grid. In contrast with GFDL's AM4.1 contributing to the sixth Coupled Model Intercomparison Project at 100 km resolution, AM4VR features much improved US climate mean patterns and variability. In particular, AM4VR shows improved representation of: precipitation seasonal‐to‐diurnal cycles and extremes, notably reducing the central US dry‐and‐warm bias; western US snowpack and summer drought, with implications for wildfires; and the North American monsoon, affecting dust storms. AM4VR exhibits excellent representation of winter precipitation, summer drought, and air pollution meteorology in California with complex terrain, enabling skillful prediction of both extreme summer ozone pollution and winter haze events in the Central Valley. AM4VR also provides vast improvements in the process‐level representations of biogenic volatile organic compound emissions, interactive dust emissions from land, and removal of air pollutants by terrestrial ecosystems. We highlight the value of increased model resolution in representing climate–air quality interactions through land‐biosphere feedbacks. AM4VR offers a novel opportunity to study global dimensions to US air quality, especially the role of Earth system feedbacks in a changing climate. Plain Language Summary: NOAA's Geophysical Fluid Dynamics Laboratory has developed a new variable‐resolution global chemistry‐climate model for research at the nexus of US climate and air quality extremes. In contrast with the global models contributing to the latest Intergovernmental Panel on Climate Change Report, this model features more than 10 times finer spatial resolution over the contiguous US, allowing it to better resolve cities, mountain valleys, thunderstorms, and urban‐to‐rural air quality variations. This model features much improved representation of regional rainfall extremes, drought, and severe air pollution events in diverse US air basins, including California. Notably, this model reduces the central US dry‐and‐warm bias that has persisted in many generations of climate models. As global climate change leads to more hot and dry weather, the resulting droughts are creating dust‐prone bare lands or stressing plants, making them less able to remove ozone pollution from the air. These effects are included in this model, with particular focus on integrating physical, chemical, and biological components at high spatial resolution to understand Earth system feedbacks to US air quality extremes in a changing climate. Key Points: A new variable‐resolution global chemistry‐climate model has been developed for research at the nexus of US climate and air quality extremesThis model unifies component advances in physics, chemistry and land‐atmosphere interactions within a seamless variable‐resolution frameworkThis model features much improved US regional precipitation, drought, and air quality extremes compared to previous models [ABSTRACT FROM AUTHOR]

Copyright of Journal of Modern Science is the property of Alcide De Gasperi University of Euroregional Economy and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Abstract The 2021 heatwave over Western North America (WNA) led to record‐breaking air temperatures and human‐perceived heat stress (humidex) values. The event was accompanied by drier conditions driven by prolonged atmospheric blocking. During the heatwave, the maximum 6‐day means of humidex and temperature (HX‐6 and TX‐6) exhibited larger anomalies (6.70 and 5.57°C) compared to the 95th percentiles (HX95 and TX95) (4.12 and 3.73°C), relative to 1981–2021 extended summer (June‐September) averages. Extreme indices of humidex show faster and larger increases than those of temperature, reflecting the nonlinear positive relationship between humidex and temperature. Future projections from a multi‐model ensemble of 19 Coupled Model Intercomparison Project Phase six (CMIP6) Global Climate Models (GCMs) clearly show an increase in humidex and temperature extremes, especially under intermediate and high emissions scenarios. Humidex indices (HX‐6 and HX95) show faster and larger increases than temperature indices (TX‐6 and TX95) for the same future years and global warming levels. Controlling for differences in GCM climate sensitivity to greenhouse gas forcing yields robust projections at various global warming levels, reducing the ranges of projected changes from the multi‐model ensemble. At 3.0°C global warming from pre‐industrial, the multi‐model ensemble projects occurrences of HX‐6, TX‐6, HX95, and TX95 over WNA that exceed 2021 levels to occur every 3.9, 1.7, 1.4, and 2.2 years, respectively, increasing to almost annually at 4.0°C.

Abstract North Atlantic midlatitude cyclones are among the most severe weather systems, causing enormous economic damages and threatening human lives. The cyclone is typically characterized by cyclonic convergent surface winds, strong updrafts, and precipitation. However, extreme surface winds are often observed within the cyclone where downdrafts develop. The present study investigates the dynamical and thermodynamical characteristics of the horizontal winds impinging on the cold frontal surface and the associated downdrafts. It is shown that the cyclonic winds into the cold frontal surface are mainly responsible for the downdrafts that transport the high‐altitude horizontal momentum to the surface and cause intense surface winds. About half of the North Atlantic midlatitude cyclones are accompanied by the downdrafts especially in the southern and western parts of the cyclone center.

Thirteen Coupled Model Intercomparison Project phase 6 (CMIP6) models were employed to simulate mean, maximum, and minimum temperature across 7 homogenous temperature regions of India for both annual and summer season (June, July, and August (JJA)). The model fidelity was assessed by comparing them with observed Climate Research Unit temperature dataset. The JJA multi‐model ensemble for the present (1981–2014) suggests large warm biases in the temperature. Although the models could simulate the spatial variability of the mean and maximum temperature over most of the homogeneous regions, they do not compare well for representing the temporal variability. We also found, that although different individual models have strengths and weaknesses in representing spatial and temporal temperature characteristics over India, a few of the models perform better than the others. For example, CNRM‐CM6 could better represent the spatial temperature patterns however they struggle in capturing the temporal variability. HadGEM3‐GC31‐LL, KACE‐1‐0G, and UKESM1‐0‐LL are comparably the best‐performing models for temporal temperature features over India. The annual maximum temperature during far future period is projected to increase by 1.5°C, 2.3°C, and 4.1°C for Socioeconomic Pathways (SSPs) SSP1‐2.6, SSP2‐4.5, and SSP5‐8.5 respectively. At regional scales, JJA mean temperature for SSP5‐8.5 revealed significant increases in Interior Peninsula (3.8°C), Western Himalaya (5.6°C), Northwest (3.9°C), West Coast (3.6°C), East Coast (3.6°C), Northeast (3.6°C), and North Central (3.8°C), highlighting the Western Himalaya's heightened sensitivity. Further, heat wave frequency is projected to rise, with the northern territories (NW, NC, NE, and part of IP) most affected, anticipating week‐long heat waves affecting around 50% of India's population under stronger SSPs. Such unprecedented impacts seem to be less profound in case of abatement scenarios such as the SSP1‐2.6. Our findings support the urgent need for more ambitious mitigation and adaptation strategies to alleviate the public health impacts of climate change. [ABSTRACT FROM AUTHOR]

A maximum temperature of 48.8°C (119.8°F) was purportedly recorded for the automated station in Siracusa (Syracuse) Contrada Monasteri, on the island of Sicilia (Sicily) Italy on August 11, 2021. A World Meteorological Organization (WMO) ad hoc evaluation committee was assembled to assess the possibility that the Sicilia temperature was the highest recorded temperature in WMO Region VI (continental only). After a detailed review of the site considerations and of the regional synoptic weather conditions, combined with detailed sensor testing and calibration by the Istituto Nazionale di Ricerca Metrologica (INRiM), the WMO evaluation committee concluded (and the Rapporteur accepted) that (1) on August 11, 2021 the high temperature recorded for the automated station in Siracusa C. da Monasteri, did reach a maximum value of 48.8°C (119.8°F), (2) that temperature is recommended to be listed as the WMO official "highest recorded temperature in WMO Region VI (continental only)" and (3) although, as the INRiM testing established, the recorded value of 48.8°C is actually an underestimate of the temperature, the committee recommend that the recorded (likely conservative) value of 48.8°C be the value listed in the Archive. An arbitrated archive of current weather and climate extremes is one means of ensuring that we have the best possible data for climate change analysis and public dissemination. [ABSTRACT FROM AUTHOR]

Cold-season windstorms represent an important, and potentially changing, geophysical hazard in the Northeastern United States. Here we employ an integrated research methodology including both a storyline approach, where three intense windstorms from the current climate are subjected to pseudo-global warming (PGW) experiments, and a long-term transient simulation using the Weather Research and Forecasting (WRF) model. An ensemble of WRF simulations is built for each windstorm using different planetary boundary layer and microphysical parameterizations. The fidelity assessment suggests all ensemble members capture the windstorm evolution in contemporary climate. The configuration with highest fidelity is used in the PGW experiments performed with perturbed temperature fields, constant relative humidity, and deiced Great Lakes. These perturbation simulations indicate some evidence for a reduction of sea level pressure and increases in wind speed over and downwind of the Great Lakes and over the Atlantic Ocean plus an increase in precipitation accumulation but a reduction in snow coverage. These changes are spatially inhomogeneous in terms of magnitude and sign but are consistent with changes in potential vorticity. Alberta Clippers and Colorado Lows dominate the cyclones responsible for historical windstorms and thus are sampled in the PGW simulations. However, the transient simulation suggests an increasing role for tropical cyclones that undergo transition to extratropical cyclones. This reinforces the value of combining information from both PGW perturbation experiments within a storyline context and transient simulations when seeking to quantify the future risk associated with cold-season windstorms under changing climate. [ABSTRACT FROM AUTHOR]

The Central Limit Theorem justifies using a normal distribution when looking at sums of many terms. In a parallel way, extreme value distributions arise in the study of maxima of many terms. The goal of this paper is to briefly review the univariate theory of extremes based on the Fisher-Tippet-Gnedenko Theorem. We then state the basics of the multivariate theory, which is significantly more complicated because it requires a measure to define the distribution. Some properties of these laws are explored, including a description of the support, an expression for the density when it exists, and some examples that illustrate possible joint dependence structures. [ABSTRACT FROM AUTHOR]

Copyright of Canadian Water Resources Journal / Revue Canadienne des Ressources Hydriques is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Compound extremes, whose socioeconomic and ecological impacts are severer than that caused by each event occurring in isolation, have evolved into a hot topic in Earth Science in the past decade. In the context of climate change, many compound extremes have exhibited increasing frequency and intensity, and shown novel fashions of combinations, posing more pressing demands and tougher challenges to scientific research and disaster prevention and response. This article, via a perspective of multi-sphere interactions within the Earth System, systematically reviews the status quo, new scientific understanding, and deficiencies regarding the definition, mechanism, change, attribution, and projection of compound extremes. This study also sorts out existing challenges and outlines a potential roadmap in advancing the study on compound extremes with respect to data requirement, mechanistic diagnosis, numerical modeling, attribution and projection, risk assessment, and adaptive response. Further directions of compound extremes studies and key research topics that warrant multi-disciplinary and multisectoral coordinated efforts are also proposed. Given that climate change has reshaped the type of extremes, a transformation from the traditional single-event perspective to a compound-event perspective is needed for scientific research, disaster prevention and mitigation, and climate change adaptation, calling for bottom-up innovation in research objects, ideas, and methods. This article will add value to promoting the research on compound extremes and interdisciplinary cooperations. [ABSTRACT FROM AUTHOR]

Many existing extremal data span only a few decades that result in large uncertainty in the estimated shape parameter of the extreme hazard model. This in turn leads to potentially large bias and variance of extrapolated extreme values at high return levels. This paper illustrates a statistical method that helps obtain hazard models that produce estimates at high return levels with reduced bias and variance. This method makes use of the maximum recorded values of extremal data that are independently recorded from a number of observational sites or generated by heterogeneous mechanisms. The logarithmically transformed average recurrence interval of the maximum recorded value at a site is shown to follow the Gumbel (Type I extreme value) distribution; therefore, multiple, say m , observational sites provide a sample of size m log-transformed average recurrence intervals, each from a distinct site. The sample can be treated as a sample from a Gumbel distribution, irrespective of the underlying hazard-generating mechanisms or the statistical hazard models. Application of the method is illustrated by analysis of the extreme wind gust data from automatic weather stations in South Australia and shown to lead to significant reduction of 2.9 m/s in potential bias of the mean and a reduction of 98.1% in variance of the 500-year gust speed at Adelaide Airport. The results are compared to the specifications in the Australian standard AS/NZS 1170.2:2021 and indicate that the standard may have overestimated the wind gust hazard; hence, the specified design wind speeds may fall on the conservative side for South Australia. [ABSTRACT FROM AUTHOR]

The presence of weather and water whiplash in Mediterranean regions of the world is analyzed using historical streamflow records from 1926 to 2023, depending on the region. Streamflow from the United States (California), Italy, Australia, Chile, and South Africa is analyzed using publicly available databases. Water whiplash—or the rapid shift of wet and dry periods—are compared. Wet and dry periods are defined based on annual deviations from the historical record average, and whiplash occurs when there is an abrupt change that overcomes an accommodated deficit or surplus. Of all the stations, there are more dry years (56%) than wet years (44%) in these regions, along with similarities in the variances and shifts in extremes (i.e., whiplash). On average, 35% of the years were defined as water whiplash years in all countries, with the highest levels in the US (California), where 42–53% of the years were whiplash years. The influence of the El Niño–Southern Oscillation (ENSO) influences Chile and South Africa strongest during the first quarter of the year. This study found that smaller extreme wet periods and larger and less extreme dry periods are prevalent in Mediterranean regions. This has implications for water management as adaptation to climate change is considered. [ABSTRACT FROM AUTHOR]

Intensity‐Duration‐Area‐Frequency (IDAF) models provide the mathematical link between precipitation intensities (I), durations (D), areas (A), and frequency of occurrence (F). They play a critical role in hydrological design, areal rainfall hazard quantification, storm characterization, and early warning system development. IDAF models extend the conventional Intensity‐Duration‐Frequency models by accounting for the spatial extent of precipitation (i.e., the area). In this study, we develop IDAF models using the entire non‐zero precipitation intensities, not only the extremes. We use the extended generalized Pareto distribution (EGPD) to model the precipitation intensities. To build the IDAF models, we adopt a data‐driven approach that allows the linkage of EGPD parameters with duration and area, based on empirically determined parametric relationships. The inference of model parameters is done using a global maximum likelihood estimation, and uncertainties are assessed by the bootstrap method. The study area is Switzerland, a topographically complex region of 42,000 km2 with regional precipitation variability and clear seasonality. The study utilizes 17 years of data from CombiPrecip, a radar‐reanalysis product developed by geostatistically merging radar and rain gauge data in an operational setting. We build the IDAF models for the spatiotemporal range of 1–72 hr and 1 to 1,089 km2 at each pixel in the study area. To the best of our knowledge, our study is the first attempt to use the EGPD in IDAF curve modeling. It discusses the use and limitations of CombiPrecip in extreme value analysis and highlights the challenges of modeling areal precipitation in a complex topographical environment. Key Points: We develop seasonal Intensity Duration Area Frequency models at every pixel location in SwitzerlandWe use all the non‐zero precipitation data and model the intensities using the extended generalized Pareto distributionWe highlight the complexity of modeling areal precipitation in mountainous regions [ABSTRACT FROM AUTHOR]

Accurate spatial estimation of extremes is an increasingly important topic in environmental research and risk assessment. Conditional bias (CB)-penalized kriging (CBPK) improves such estimation by minimizing linearly weighted sum of error variance and variance of Type-II error. However, CBPK requires skillful prescription of the weight for the CB penalty which is a significant challenge in practice. In this paper, we describe an extension of CBPK, referred to herein as adaptive conditional bias-penalized kriging (ACBPK), which objectively prescribes the weight for improved estimation of extremes without deteriorating performance in the unconditional mean squared error sense. For comparative evaluation in the real world, cross validation experiments were carried out for precipitation estimation using hourly rain gauge data in the Arkansas-Red River Basin (AB), central Texas (TX) and southeastern US (SE) areas. The results show that CB is detected for about 26, 24 and 25% of all data points in the AB, TX and SE cases, respectively, and that, given detection of CB, ACBPK reduces root mean square error of hourly precipitation exceeding 12.7 mm by 15, 21 and 9% and hourly precipitation exceeding 25.4 mm by 14, 26 and 10% relative to ordinary kriging (OK) for the AB, TX and SE cases, respectively. The overall findings indicate that, if accurate spatial estimation in the tails of the distribution is important or accurate modeling of spatiotemporally-varying correlation structure is a challenge, ACBPK should be favored over OK. [ABSTRACT FROM AUTHOR]

We consider issues related to the effect of climate change on the persistence of (trend‐corrected) temperatures using global gridded data for both land and oceans. We first discuss how the presence of trends and additive noise affects inference about persistence. Ignoring a trend induces an upward bias, while not accounting for noise induces a downward bias. We show that the increase in persistence in the commonly used Warm Spell Duration Index is simply an artifact of increasing temperatures. To purge the impact of both trends and noise, we adopt a simple state‐space model. Of separate interest, we document a much larger noise component for land than for oceans. The estimates of the persistence are much larger for oceans than for land. Inspection of the estimates across various subsamples and the application of tests for structural changes suggest the same pattern of persistence for both land and oceans across time, with few minor exceptions. Hence, our results show that surface temperature persistence has remained constant during the observed period. [ABSTRACT FROM AUTHOR]

Relative to drought and heat waves, the effect of winter warming on forest CO2 fluxes during the dormant season has less been investigated, despite its relevance for net CO2 uptake in colder regions with higher carbon content in soils. Our objective was to test the effect of the exceptionally warm winter in 2020 on the winter CO2 budget of cold-adapted evergreen needleleaf forests across Europe, and identify the contribution of soil and air temperature to changes in winter CO2 fluxes in response to warming. Our hypothesis was that warming in winter leads to higher emissions across colder sites due to increased ecosystem respiration. To test this hypothesis, we used 98 site-year eddy covariance measurements across 14 evergreen needleleaf forests (ENFs) distributed from north to south of Europe (from Sweden to Italy). We used a data-driven approach to quantify the effect of air and soil temperature on changes in net ecosystem productivity (NEP) during the warm winter of 2020. Our results showed that the impact of warming was different across sites, as in the lower altitude and lower latitude sites positive soil temperature anomalies were larger, while positive air temperature anomalies were larger in the northern latitude and high-altitude sites. Warming in winter led to a divergent response across the sites. Out of 14 sites only in 3 sites net ecosystem productivity declined in winter significantly in response to warming. In addition, we observed that in the colder sites daytime NEP (that is dominated by photosynthesis) declined with warming of the air in winter, whereas in the warmer sites daytime NEP increased with warming of the soil. While warming increases ecosystem respiration, it might not trigger productivity in winter if the soil within the rooting zone remains frozen. Forests within the same plant functional type category can exhibit differing reactions to winter warming and to predict their responses accurately it is crucial to account for variations in local climate, physiology, and structure simultaneously. [ABSTRACT FROM AUTHOR]

Abstract Quantifying the risk from extreme weather events in a changing climate is essential for developing effective adaptation and mitigation strategies. Climate models capturing different scenarios are often the starting point for physical risk. However, accurate risk assessment for mitigation and adaptation often demands a level of detail they typically cannot resolve. Here, we develop a dynamic data‐driven downscaling (super‐resolution) method that incorporates physics and statistics in a generative framework to learn the fine‐scale spatial details of rainfall. Our approach transforms coarse‐resolution (0.25°) climate model outputs into high‐resolution (0.01°) rainfall fields while efficaciously quantifying the hazard and its uncertainty. The downscaled rainfall fields closely match observed spatial fields and their distributions. Contrary to conventional thinking, our results suggest that coupling simple statistics and physics to learning improves the efficacy of downscaling midlatitude rainfall extremes from climate models.

Abstract Accurate modeling of snow depth (SD) processes is critical for understanding global energy balance changes, affecting climate change mitigation strategies. This study evaluates the Coupled Model Intercomparison Project Phase 6 (CMIP6) model performance in simulating daily SD across Canada. We assess CMIP6 outputs against observed data, focusing on daily SD averages, snow cover durations, and rates of accumulation and depletion, alongside annual SD peaks for 11 major Canadian catchments. Our findings reveal that CMIP6 simulations generally overestimate daily SD by 57.7% and extend snow cover duration by 30.5 days on average. While three models (CESM2, UKESM1‐0‐LL and MIROC6) notably align with observed annual SD peaks, simulation biases suggest the need for enhanced model parameterization to accurately capture snow physics, particularly in regions with permanent snow cover and complex terrains. This analysis underscores the necessity of refining CMIP6 simulations and incorporating detailed geographical data for better SD predictions.

In 2015, the United Nations General Assembly launched seventeen Sustainable Development Goals (SDGs), aimed at being achieved by 2030. The SDGs 6 (clean water & sanitation) and 13 (climate actions) are two critically important goals concerning water resources that need to be addressed. This study used the Scopus database to explore climate extremes, specifically droughts and floods, in East Asia and highlight the region's efforts and contributions towards achieving SDGs 6 and 13. We found that even before the implementation of SDGs, the topics related to solving the problems of water resources, water quality, and treatment of wastewater using different conceptual models and methodologies were the main concerns in the region. The adoption of SDGs has led to a heightened focus on water and climate sustainability in East Asia, with the considerable surge in climate-related studies after 2019. Under SDG 13, all countries have contributed substantially to climate action research. Keyword analysis indicates that climate change, water management, water treatment, water quality, and adsorption remain prominent. SDGs 6 and 13 have emerged as crucial areas of focus for research and initiatives as the global community grapples with escalating water resources and climate challenges. Under specific keywords search, China has 2nd place in the search with climate and water during the SDGs period, accounting for 21 % of the entire publication from 2015 to 2023. Japan and South Korea account for of 4 % and 3 %, respectively. The research on floods and droughts has garnered significant attention, with half of the ten highly co-cited literature examining the changing pattern of drought, the influence of extreme events on crop yield, and other related topics. Despite the positive contribution of the East Asia region towards SDGs 6 and 13, there is still an urgent need for a more robust framework to improve the complex interconnections between climate actions, clean water, and sanitation for a sustainable soil-water-plant-atmosphere ecosystem.

Storm surges are the most important driver of flooding in many coastal areas. Understanding the spatial extent of storm surge events has important financial and practical implications for flood risk management, reinsurance, infrastructure reliability and emergency response. In this paper, we apply a new tracking algorithm to a high-resolution surge hindcast (CODEC, 1980–2017) to characterize the spatial dependence and temporal evolution of extreme surge events along the coastline of the UK and Ireland. We quantify the severity of each spatial event based on its footprint extremity to select and rank the collection of events. Several surge footprint types are obtained based on the most impacted coastal stretch from each particular event, and these are linked to the driving storm tracks. Using the collection of the extreme surge events, we assess the spatial distribution and interannual variability of the duration, size, severity, and type. We find that the northeast coastline is most impacted by the longest and largest storm surge events, while the English Channel experiences the shortest and smallest storm surge events. The interannual variability indicates that the winter seasons of 1989-90 and 2013–14 were the most serious in terms of the number of events and their severity, based on the return period along the affected coastlines. The most extreme surge event and the highest number of events occurred in the winter season 1989–90, while the proportion of events with larger severities was higher during the winter season 2013–14. This new spatial analysis approach of surge extremes allows us to distinguish several categories of spatial footprints of events around the UK/Ireland coast and link these to distinct storm tracks. The spatial dependence structures detected can improve multivariate statistical methods which are crucial inputs to coastal flooding assessments.

Abstract Australia is one of the regions strongly affected by the El Niño‐Southern Oscillation (ENSO). The recent 2020–2023 La Niña event was marked by record‐breaking rainfall and flooding across eastern Australia. The continuous wet conditions during the triple La Niña motivated us to explore the impacts of single‐year and multi‐year ENSO events on Australian rainfall using observational data sets. We find that, while there is no difference in the rainfall impacts during single or double El Niño events, Australian rainfall tends to increase in the third year of triple La Niña events compared to the first and second years. The enhanced rainfall impact during the third La Niña year occurs despite no strengthening of La Niña in the tropical Pacific, suggesting that other processes such as local rainfall‐soil moisture feedback may play a role in prolonging the effects of multi‐year La Niña events in Australia.

Abstract We present a variable‐resolution global chemistry‐climate model (AM4VR) developed at NOAA's Geophysical Fluid Dynamics Laboratory (GFDL) for research at the nexus of US climate and air quality extremes. AM4VR has a horizontal resolution of 13 km over the US, allowing it to resolve urban‐to‐rural chemical regimes, mesoscale convective systems, and land‐surface heterogeneity. With the resolution gradually reducing to 100 km over the Indian Ocean, we achieve multi‐decadal simulations driven by observed sea surface temperatures at 50% of the computational cost for a 25‐km uniform‐resolution grid. In contrast with GFDL's AM4.1 contributing to the sixth Coupled Model Intercomparison Project at 100 km resolution, AM4VR features much improved US climate mean patterns and variability. In particular, AM4VR shows improved representation of: precipitation seasonal‐to‐diurnal cycles and extremes, notably reducing the central US dry‐and‐warm bias; western US snowpack and summer drought, with implications for wildfires; and the North American monsoon, affecting dust storms. AM4VR exhibits excellent representation of winter precipitation, summer drought, and air pollution meteorology in California with complex terrain, enabling skillful prediction of both extreme summer ozone pollution and winter haze events in the Central Valley. AM4VR also provides vast improvements in the process‐level representations of biogenic volatile organic compound emissions, interactive dust emissions from land, and removal of air pollutants by terrestrial ecosystems. We highlight the value of increased model resolution in representing climate–air quality interactions through land‐biosphere feedbacks. AM4VR offers a novel opportunity to study global dimensions to US air quality, especially the role of Earth system feedbacks in a changing climate.

In recognition of the increased risk to national resilience from flooding, we provide an overview of recent and future improvements to flood risk forecasting and communication at the Flood Forecasting Centre (FFC). We draw on the analysis of fluvial and surface water flooding across England and Wales in 2021 to highlight these areas of improvement. Already implemented improvements in both the underpinning science and our long lead-time product are described in the context of high-magnitude, high-impact floods. In addition, we consider more substantial developments from improved modelling of convection to translating this to surface water flood risk and to the essential communication and service provision. Finally, recognising that many of the challenges are shared internationally, we distil our key recommendations for future improvement. These improvements rely on collaboration for them to be successful. HIGHLIGHTS We consider the most noteworthy floods that affected England and Wales in 2021 and suggest developments and further improvements to both our services and the underpinning science.; We describe where service improvements have already been implemented in our longer lead time service, the 30 day Flood Outlook.; We highlight underlying improvements to our science, drawing on the evolution of weather types from multi-model Decider, the Surface Water Flooding Hazard Impact Model (SWFHIM), and improvements from high resolution modelling.; Future recommendations for improvement are then highlighted, illustrating the need for international collaboration, the co-design and co-creation of future services with users.;

Flooding in 2021 has highlighted the increased risk to national resilience. This is against a backdrop of the UK climate projected to become more extreme over the next few decades. This paper considers the notable river and surface water flood events within England and Wales during 2021 and examines the performance of the Flood Forecasting Centre in highlighting the flood risk to our customers. We reflect on team debriefs as well as feedback and surveys from our customers. We distil our learnings and make connections with improvements in our underpinning science, forecasting tools, products and services. Finally, we highlight challenges associated with surface water flooding and suggest how we may collectively start to overcome these. HIGHLIGHTS We provide an overview of the most noteworthy floods that affected England and Wales in 2021 and outline key products and services provided by the Flood Forecasting Centre (FFC).; Our post flood event reviews are summarised and our reflections on these 2021 events are highlighted.; Key findings from our customer reviews are provided.; Future recommendations for improvements are then highlighted, illustrating the need for international collaboration and the co-design and co-creation of future services with users.;

Precipitation prediction at seasonal timescales is important for planning and management of water resources as well as preparedness for hazards such as floods, droughts and wildfires. Quantifying predictability is quite challenging as a consequence of a large number of potential drivers, varying antecedent conditions, and small sample size of high-quality observations available at seasonal timescales, that in turn, increases prediction uncertainty and the risk of model overfitting. Here, we introduce a generalized probabilistic framework to account for these issues and assess predictability under uncertainty. We focus on prediction of winter (Nov-Mar) precipitation across the contiguous United States, using sea surface temperature-derived indices (averaged in Aug-Oct) as predictors. In our analysis we identify "predictability hotspots," which we define as regions where precipitation is inherently more predictable. Our framework estimates the entire predictive distribution of precipitation using copulas and quantifies prediction uncertainties, while employing principal component analysis for dimensionality reduction and a cross validation technique to avoid overfitting. We also evaluate how predictability changes across different quantiles of the precipitation distribution (dry, normal, wet amounts) using a multi-category 3 × 3 contingency table. Our results indicate that well-defined predictability hotspots occur in the Southwest and Southeast. Moreover, extreme dry and wet conditions are shown to be relatively more predictable compared to normal conditions. Our study may help with water resources management in several subregions of the United States and can be used to assess the fidelity of earth system models in successfully representing teleconnections and predictability.

Precipitation is an essential element in earth system research, which greatly benefits from the emergence of Satellite Precipitation Products (SPPs). Therefore, assessment of the accuracy of the SPPs is necessary both scientifically and practically. The Integrated Multi-Satellite Retrievals for GPM (IMERG) is one of the most widely used SPPs in the scientific community. However, there is a lack of comprehensive evaluation for the performance of the newly released IMERG Version 07, which is essential for determining its effectiveness and reliability in precipitation estimation. In this study, we compare the IMERG V07 Final Run (V07_FR) with its predecessor IMERG V06_FR across scales from January 2016 to December 2020 over the globe (cross-compare their similarities and differences) and a focused study on mainland China (validate against 2481 rain gauges). The results show that: (1) Globally, the annual mean precipitation of V07_FR increases 2.2% compared to V06_FR over land but decreases 5.8% over the ocean. The two SPPs further exhibit great differences as indicated by the Critical Success Index (CSI = 0.64) and the Root Mean Squared Difference (RMSD = 3.42 mm/day) as compared to V06_FR to V07_FR. (2) Over mainland China, V06_FR and V07_FR detect comparable precipitation annually. However, the Probability of Detection (POD) improves by 5.0%, and the RMSD decreases by 3.7% when analyzed by grid cells. Further, the POD (+0%~+6.1%) and CSI (+0%~+8.8%) increase and the RMSD (−11.1%~0%) decreases regardless of the sub-regions. (3) Under extreme rainfall rates, V07_FR measures 4.5% lower extreme rainfall rates than V06_FR across mainland China. But V07_FR tends to detect more accurate extreme precipitation at both daily and event scales. These results can be of value for further SPP development, application in climatological and hydrological modeling, and risk analysis. [ABSTRACT FROM AUTHOR]

Flooding in 2021 has highlighted the increased risk to national resilience. This is against a backdrop of the UK climate projected to become more extreme over the next few decades. This paper considers the notable river and surface water flood events within England and Wales during 2021 and examines the performance of the Flood Forecasting Centre in highlighting the flood risk to our customers. We reflect on team debriefs as well as feedback and surveys from our customers. We distil our learnings and make connections with improvements in our underpinning science, forecasting tools, products and services. Finally, we highlight challenges associated with surface water flooding and suggest how we may collectively start to overcome these. [ABSTRACT FROM AUTHOR]