Extreme Precipitation Patterns Research Articles

Diurnal Cycle of Precipitation and Extremes in Nepal

This study examines the spatio-temporal distribution of hourly precipitation across Nepal using Integrated Multi-Satellite Retrievals for Global Precipitation Measurement (IMERG) data from 2015 to 2021. The findings indicate that hourly precipitation intensity during the monsoon season can reach up to 0.7 mm/hr, while pre-monsoon intensities peak at 0.2 mm/hr. These intensities are predominantly concentrated in mid- and low-elevation areas of central and eastern Nepal. Post-monsoon and winter seasons exhibit high-intensity precipitation patches over high-elevation regions in the western, central, and eastern peripheries of the country. The annual distribution of hourly precipitation shows a pronounced peak during the monsoon season, indicating that a significant portion of the total annual precipitation occurs during this period. Extreme precipitation events follow a similar seasonal distribution, with monsoon extremes exceeding 15 mm/hr. The diurnal cycle of monsoonal precipitation shows unique characteristics, peaking at 0.65 mm/hr around midnight and decreasing to 0.2 mm/hr by late morning, then increasing steadily in the afternoon and evening. Additionally, the study contrasts hourly heavy precipitation extremes (≥10 mm/hr or day) with daily extremes, noting that hourly extremes, despite being accumulated into daily measures, present a higher frequency, and pose greater short-term hazard risks. This analysis over a seven-year period suggests the need for continued research to determine spatial and temporal trends in hourly versus daily extreme precipitation patterns, particularly in the context of climate change and its impacts on regional hydrology and meteorology.

Spatiotemporal Patterns of Typhoon-Induced Extreme Precipitation in Hainan Island, China, 2000–2020, Using Satellite-Derived Precipitation Data

Spatiotemporal Patterns of Typhoon-Induced Extreme Precipitation in Hainan Island, China, 2000–2020, Using Satellite-Derived Precipitation Data

Temporal Distribution of Extreme Precipitation in Barcelona (Spain) under Multi-Fractal n-Index with Breaking Point

Rainfall regimes are experiencing variations due to climate change, and these variations are adequately simulated by Earth System Models at a daily scale for most regions. However, there are not enough raw outputs to study extreme and sub-daily precipitation patterns on a local scale. To address this challenge, Monjo developed the n-index by characterizing the intensity and concentration of precipitation based on mono-fractal theory. In this study, we explore the use of a multi-fractal approach to establish a more accurate method of time scaling useful to study extreme precipitation events at a finer temporal resolution. This study was carried out on the reference station of Barcelona (Spain) and its surroundings in order to be representative of the Mediterranean climate. For return periods between 2 and 50 years, two variables were analyzed: the n-index and the reference intensity I0. Moreover, a new parameter, the so-called “breaking point”, was designed here to describe the reference intensity I0, which is predominant for low time ranges. The results showed that both parameters are dependent on the time steps and the return period, and the scores confirmed the validity of our approach. Finally, the n-index was projected under downscaled CMIP6 climate scenarios by 2100, showing a sustained increase of up to +10%.

Climate-informed flood risk mapping using a GAN-based approach (ExGAN)

This study develops a class of robust models for flood risk mapping in highly vulnerable regions by focusing on accurately depicting extreme precipitation patterns aligned with regional climates. By implementing sophisticated hydrodynamics modeling and advanced probabilistic approaches, the present work underscores the efficacy of physical-based methodologies in the flood risk assessment. We propose a machine learning based ExGAN to address the challenge of synthesizing extreme precipitation scenarios which faithfully capture the nuances of local climatology. It is expected that through refined temporal disaggregation, the ExGAN approach exhibits exceptional proficiency in replicating a diverse spectrum of extreme precipitation patterns specific to the vulnerable region under scrutiny. Therefore, using these synthesized scenarios as inputs in a meticulously calibrated hydrological model would enable a comprehensive and detailed flood risk mapping exercise. To demonstrate the robustness of the developed mode, we perform a rigorous testing and validation within the highly susceptible Martil river basin, situated in the northern Mediterranean region of Morocco. The obtained results confirm that extending return periods would provide invaluable insights into the expanding geographical expanse of at-risk areas, clarifying the evolving landscape of vulnerability rather than merely amplifying inherent risk levels. Comparisons against the conventional Monte-Carlo sampling are also carried out in this study and the obtained results highlight significant overestimations within the latter, emphasizing the imperative need to account for diverse uncertainties beyond the basic sampling strategies within the realm of hydrodynamic modeling.

Changes in Convective Precipitation Reflectivity over the CONUS Revealed by High-Resolution Radar Observations from 2015 to 2021

The change in extreme precipitation events in the conterminous United States (CONUS) has been of interest to the research communities in recent years for its intensification under environmental and climate change. Previous studies have not yet used sub-hourly precipitation observations to examine convective precipitation change over the CONUS. This study aims to fill the gap by examining convective precipitation, identified by radar reflectivity, in the CONUS using the state-of-the-art Multi-radar Multi-sensor data, operated at the NOAA/National Severe Storms Laboratory, with an unprecedentedly high spatial (1 km) and temporal (2 min) resolutions. These high-resolution data are expected to better capture the precipitation peak and the precipitation pattern. The results showed that in CONUS, precipitation reflectivity increased both in magnitude and the number of convective days from 2015 to 2021. For example, in 2019, 60% of areas showed an increase in the magnitude of precipitation, and the average number of convective days over CONUS has increased by 19%. Changes in precipitation also vary by season and region. This study highlights the need for continued monitoring and understanding of the evolving pattern of extreme precipitation in the CONUS, especially at sub-hourly frequency, as it exposes significant impacts on various sectors, including agriculture, infrastructure, and human health.

Projected Changes in Extreme Precipitation Patterns across Algerian Sub-Regions

Extreme precipitation events play a crucial role in shaping the vulnerability of regions like Algeria to the impacts of climate change. To delve deeper into this critical aspect, this study investigates the changing patterns of extreme precipitation across five sub-regions of Algeria using data from 33 model simulations provided by the NASA Earth Exchange Global Daily Downscaled Climate Projections (NEX-GDDP-CMIP6). Our analysis reveals a projected decline in annual precipitation for four of these regions, contrasting with an expected increase in desert areas where annual precipitation levels remain low, typically not exceeding 120 mm. Furthermore, key precipitation indices such as maximum 1-day precipitation (Rx1day) and extremely wet-day precipitation (R99p) consistently show upward trends across all zones, under both SSP245 and SSP585 scenarios. However, the number of heavy precipitation days (R20mm) demonstrates varied trends among zones, exhibiting stable fluctuations. These findings provide valuable foresight into future precipitation patterns, offering essential insights for policymakers and stakeholders. By anticipating these changes, adaptive strategies can be devised to mitigate potential climate change impacts on crucial sectors such as agriculture, flooding, water resources, and drought.

Meteorological disaster disturbances on the main crops in the north‒south transitional zone of China

Global climate change, with warming as its main feature, has altered the spatial-temporal evolution of factors such as precipitation and temperature that can cause meteorological disasters. The complex and changeable climate has led to frequent natural disasters, while the frequency and intensity of extreme climate events have also significantly increased, posing an enormous threat to societal production and human life. As the most important geoecological transitional zone of mainland China, the stability of agricultural production in China’s north–south transitional zone is crucial for ensuring food security under climate change. With the use of daily precipitation and potential evapotranspiration data from 1961 to 2018, this study focused on analysing disturbances such as extreme precipitation and drought disasters at different time scales during the winter wheat and summer maize growing seasons in the north–south transitional zone of China from an agricultural production perspective and attempted to answer the following questions: first, from an agricultural production perspective, what are the temporal and spatial distribution patterns of extreme precipitation and arid climate events in the north–south transitional zone? Second, which areas are at high risk of being disturbed by different types of meteorological disasters and require increased attention? The results indicated that (1) in terms of the overall temporal variation, the degree of extreme precipitation and drought stress faced by agricultural production in the region is decreasing. However, the temporal variation at each station in the north–south transitional zone was not completely consistent with the overall trend, and both increasing and decreasing trends were observed. The sites exhibiting an increase overlapped with typical regions of the north–south transitional zone to varying degrees, indicating that the typical regions represented not only theoretical potential risk areas under climate change but also suffered from meteorological disaster disturbances. (2) The precipitation distribution during the winter wheat growth period in the south–north transitional zone was uneven and varied significantly. High values of extreme precipitation indices during the winter wheat growth period were mainly concentrated in the southern part of the eastern section of the north‒south transitional zone. The precipitation distribution during the summer maize growth period significantly differed, with the highest amount of heavy rain and largest number of rainstorm days concentrated in the southeastern part of the north‒south transitional zone. The spatial distribution of the drought frequency in the north–south transitional zone, as indicated by the monthly standardized precipitation evapotranspiration index (SPEI1), showed that the areas with high total drought frequencies were mainly concentrated in northeast Jiangsu, southeast Henan, and north Anhui, which primarily experienced light drought. The central part of Jiangsu Province exhibited a high frequency of moderate drought, while southern Jiangsu Province and southwestern Shaanxi Province were prone to severe drought. Additionally, southeastern Hebei and eastern Henan were identified as areas with a high frequency of extreme drought. Finally, the central region of Sichuan Province was characterized by both severe and extreme drought conditions. Based on the SPEI12-derived spatial distribution of the drought frequency in the north–south transitional zone, the areas with a high total drought frequency were mainly concentrated in central and eastern Henan, southeast Shaanxi, southeast Shandong, and central Sichuan, which primarily experienced light to moderate drought. The northwestern part of Jiangsu, the southern part of Hebei, and the western part of Shandong are regions with a high frequency of severe drought, while the eastern part of Henan is an area with high frequencies of both severe and extreme drought. (3) High-value areas of extreme precipitation and drought disturbance in the north–south transitional zone overlapped with the edge of the transitional zone to varying degrees. Approximately 63.58% of the north‒south transitional zone of China was characterized by moderate or high stress levels, primarily concentrated along the southern boundary and central core area, and nearly 39.5% of all counties experienced two or more types of disaster stresses.

Impacts of El Niño diversity on East Asian summertime precipitation extremes

This study examines the impacts of Eastern Pacific (EP) and Central Pacific (CP) El Niño on summertime extreme precipitation over East Asia during the El Niño decay phase. The findings reveal distinct patterns of extreme precipitation for the two El Niño types. During EP El Niño, more intense extreme precipitation occurs over south of the Yangtze River (SYR), while suppressed extremes are observed over Mei-Yu rainband in China, Baiu in Japan, and Changma in South Korea (MBC). Conversely, CP El Niño leads to weaker (stronger) extreme precipitation over SYR (MBC). This study also differentiates between tropical cyclone (TC) and non-TC related precipitation, showing that TCs have minimal influence on the overall extreme precipitation compared to non-TC related events. Further investigation reveals that EP and CP El Niño distinctly influence atmospheric circulation patterns, thereby causing different distributions of non-TC extreme precipitation. Specifically, during EP El Niño, sea surface temperature (SST) warming signals in the tropical Indian Ocean induce the southward displacement of the South Asia High (SAH), westerly jet (WJ), and Western Pacific subtropical high (WPSH). This amplifies moisture flux convergence, elevating the likelihood of intense extreme precipitation over SYR. Conversely, MBC experiences moisture flux divergence, resulting in fewer precipitation extremes. During CP El Niño, SST warming signals in the Maritime Continent prompt the northward shift of SAH, WJ, and WPSH by modulating local Hadley circulations. These anomalies lead to strengthened moisture convergence (divergence) over MBC (SYR), consequently resulting in a higher (lower) likelihood of intense extreme precipitation over MBC (SYR).

An increase in widespread extreme precipitation events during the northeast monsoon season over south peninsular India

While the spatio-temporal characteristics of Indian summer monsoon precipitation and its extreme spells have been extensively studied, the northeast monsoon, which occurs from October to December (i.e., post-monsoon season) and affects the southern peninsula of India, has not received as much attention. In light of this, the present study explores the spatio-temporal characteristics of precipitation during the northeast monsoon, with a particular emphasis on widespread extreme precipitation events and their associated large-scale synoptic systems, using recent ensemble of high-resolution regional climate models (RCMs) simulations and the Indian monsoon data assimilation and analysis (IMDAA) reanalysis. The study reveals that both models tend to underestimate the intensity and frequency of observed precipitation events, although their skills in reproducing the observed spatial patterns of both mean and extreme precipitation are quite high (r > 0.75). A substantial increase in widespread extreme precipitation events (nearly twofold), along with a 30% rise in precipitation intensity, has been observed in the recent decade compared to the 1980s, and models demonstrate a similar directional change but tend to underestimate the magnitude of observed precipitation. This increase appears to be linked to the rapid warming of the Indian Ocean, which, in turn, increases the water vapor in the atmosphere, ultimately supplying more moisture to the southeastern peninsular India. On the other hand, observed discrepancies in replicating some of the reported widespread impactful extreme precipitation events in the years 2007 and 2015 over the southern India region underscore the need for caution when interpreting model simulations. Low-pressure systems, such as troughs, associated with cyclonic circulations originating from the Bay of Bengal, have been identified as the primary sources of moisture fueling heavy precipitation during these events. Cluster analysis highlights varying synoptic patterns within the general framework, emphasizing the need for a more nuanced approach in simulating and forecasting extreme precipitation events. Overall, this study underscores the importance of enhancing modeling capabilities to better understand and prepare for the growing challenges posed by extreme precipitation events.

Climatic characteristics and main weather patterns of extreme precipitation in the middle Yangtze River valley

Abstract Based on the daily precipitation data and ERA5 reanalysis data of 40 years from 1981 to 2018 in the middle Yangtze River Valley (MYRV), the climatic characteristics of extreme precipitation are analyzed using statistical methods. The multivariate empirical orthogonal functions and spectral clustering methods are used to classify and synthesize the extreme precipitation weather. The results show that: (1) The spatial distribution of the extreme precipitation threshold is uneven due to the regional topography. The spatial distribution of the average precipitation and frequency of extreme precipitation days is characterized by the north-south antiphase distribution. (2) According to the main influencing systems, the 215 regional extreme precipitation days in the MYRV in the past 40 years can be classified into three types: southwest vortex type, typhoon type, and cold trough shear line type. (3) The southwest vortex type of extreme precipitation occurs in the deep warm and humid airflow in front of the southwest vortex trough, but the typhoon type has better thermal dynamic conditions, and the cold and warm airflow convergence of the cold trough shear line type is more obvious. The rainfall area of three types of extreme precipitation is the result of the synergistic effect of the system.

Projections of Mean and Extreme Precipitation Using the CMIP6 Model: A Study of the Yangtze River Basin in China

In this study, we conducted an analysis of the CN05.1 daily precipitation observation dataset spanning from 1985 to 2014. Subsequently, we ranked the 30 global climate model datasets within the NEX-GDDP-CMIP6 dataset using the RS rank score method. Multi-model weighted-ensemble averaging was then performed based on these RS scores, followed by a revision of the multi-model weighted-ensemble averaging (rs-MME) using the quantile mapping method. The revised rs-MME model data were utilized for simulating precipitation variations within the Yangtze River Basin. We specifically selected 11 extreme-precipitation indices to comprehensively evaluate the capability of the revised rs-MME model data in simulating extreme-precipitation occurrences in the region. Our investigation culminated in predicting the characteristics of precipitation and the potential shifts in extreme-precipitation patterns across the region under three distinct shared socioeconomic pathways (SSP1-2.6, SSP2-4.5, and SSP5-8.5) for three temporal segments: the Near 21C (2021–2040), Mid 21C (2041–2070), and Late 21C (2071–2100). Our findings reveal that the revised rs-MME model data effectively resolve the issues of the overestimation and underestimation of precipitation data present in the previous model. This leads to an enhanced simulation of mean annual precipitation, the 95th percentile of precipitation, and the extreme-precipitation index for the historical period. However, there are shortcomings in the simulation of linear trends in mean annual precipitation, alongside a significant overestimation of the CWD and CDD indices. Furthermore, our analysis forecasts a noteworthy increase in future mean annual precipitation within the Yangtze River Basin region, with a proportional rise in forced radiation across varying scenarios. Notably, an ascending trend of precipitation is detected at the headwaters of the Yangtze River Basin, specifically under the Late 21C SSP5-8.5 scenario, while a descending trend is observed in other scenarios. Conversely, there is an escalating pattern of precipitation within the middle and lower reaches of the Yangtze River Basin, with most higher-rate changes situated in the middle reaches. Regarding extreme-precipitation indices, similar to the annual average precipitation, a remarkable upsurge is evident in the middle and lower reaches of the Yangtze River Basin, whereas a relatively modest increasing trend prevails at the headwaters of the Yangtze River Basin. Notably, the SSP5-8.5 scenario portrays a substantial increase in extreme-precipitation indices.

Impact of climate extreme events and their causality on maize yield in South Africa

Extreme climate events can have a significant negative impact on maize productivity, resulting in food scarcity and socioeconomic losses. Thus, quantifying their effect is needed for developing future adaptation and mitigation strategies, especially for countries relying on maize as a staple crop, such as South Africa. While several studies have analyzed the impact of climate extremes on maize yields in South Africa, little is known on the quantitative contribution of combined extreme events to maize yield variability and the causality link of extreme events. This study uses existing stress indices to investigate temporal and spatial patterns of heatwaves, drought, and extreme precipitation during maize growing season between 1986/87 and 2015/16 for South Africa provinces and at national level and quantifies their contribution to yield variability. A causal discovery algorithm was applied to investigate the causal relationship among extreme events. At the province and national levels, heatwaves and extreme precipitation showed no significant trend. However, drought severity increased in several provinces. The modified Combined Stress Index (CSIm) model showed that the maize yield nationwide was associated with drought events (explaining 25% of maize yield variability). Heatwaves has significant influence on maize yield variability (35%) in Free State. In North West province, the maize yield variability (46%) was sensitive to the combination of drought and extreme precipitation. The causal analysis suggests that the occurrence of heatwaves intensified drought, while a causal link between heatwaves and extreme precipitation was not detected. The presented findings provide a deeper insight into the sensitivity of yield data to climate extremes and serve as a basis for future studies on maize yield anomalies.

Extreme precipitation patterns in the Asia–Pacific region and its correlation with El Niño-Southern Oscillation (ENSO)

In the Asia–Pacific region (APR), extreme precipitation is one of the most critical climate stressors, affecting 60% of the population and adding pressure to governance, economic, environmental, and public health challenges. In this study, we analyzed extreme precipitation spatiotemporal trends in APR using 11 different indices and revealed the dominant factors governing precipitation amount by attributing its variability to precipitation frequency and intensity. We further investigated how these extreme precipitation indices are influenced by El Niño-Southern Oscillation (ENSO) at a seasonal scale. The analysis covered 465 ERA5 (the fifth-generation atmospheric reanalysis of the European Center for Medium-Range Weather Forecasts) study locations over eight countries and regions during 1990–2019. Results revealed a general decrease indicated by the extreme precipitation indices (e.g., the annual total amount of wet-day precipitation, average intensity of wet-day precipitation), particularly in central-eastern China, Bangladesh, eastern India, Peninsular Malaysia and Indonesia. We observed that the seasonal variability of the amount of wet-day precipitation in most locations in China and India are dominated by precipitation intensity in June–August (JJA), and by precipitation frequency in December–February (DJF). Locations in Malaysia and Indonesia are mostly dominated by precipitation intensity in March–May (MAM) and DJF. During ENSO positive phase, significant negative anomalies in seasonal precipitation indices (amount of wet-day precipitation, number of wet days and intensity of wet-day precipitation) were observed in Indonesia, while opposite results were observed for ENSO negative phase. These findings revealing patterns and drivers for extreme precipitation in APR may inform climate change adaptation and disaster risk reduction strategies in the study region.

Exploring the uncertainty of weather generators’ extreme estimates in different practical available information scenarios

ABSTRACT Stochastic weather generators are powerful tools capable of extending the available precipitation records to the desired length. These, however, rely upon the amount of information available, which often is scarce, especially in arid and semi-arid regions. No studies can be found dealing with the uncertainty associated with these estimates related to the amount of information used in the weather generation calibration process, which is precisely the aim of the present study. A Monte Carlo simulation from a synthetic population was performed, evaluating the uncertainty of the simulated quantiles in different practical available information scenarios. The results showed that incorporating a regional study of annual maximum daily precipitation in the model parameterization clearly reduced the uncertainty of all quantile estimates. In addition, it has been proved that the uncertainty of these estimates increases with the population extremality, thus marking the importance of integrating additional information in regions with extreme precipitation patterns.

Time distribution pattern and spatial heterogeneity of hourly scale event-based extreme precipitation in China

Extreme precipitation occurs naturally in the form of “events,” and the spatio-temporal distribution pattern of event-based extreme precipitation is of great significance to flood disaster prevention and water resources protection. This study proposed the concept of hourly scale event-based extreme precipitation (HEEP), referring to a precipitation event with at least an hourly precipitation extreme (exceeding the 99th percentile threshold). Then we identified the dominant time distribution pattern (TDP), analyzed the seasonal proportion and distribution, and investigated the spatial distribution of three basic features (total frequency, amount, and duration) of HEEP from 2008 to 2017 in China and its nine major river basins. Results showed that over 90% of the areas in China were dominated by short-duration HEEP (S-HEEP, less than eight hours). While the rest areas were led by long-duration HEEP (L-HEEP, more than eight hours), mainly concentrated on the middle and lower reaches of the Yangtze River and southeast coastal areas of China. Areas dominated by S-HEEP were more than L-HEEP in different seasons except in winter. Three basic features of HEEP showed similar spatial distribution patterns of high in southeastern China and low in northwestern China, indicating HEEP frequently occurred in southeastern China with relatively large amounts and long duration. The concept of HEEP can provide new ideas for regional and global extreme precipitation research.

Pitfalls of Climate Network Construction—A Statistical Perspective

Abstract Network-based analyses of dynamical systems have become increasingly popular in climate science. Here, we address network construction from a statistical perspective and highlight the often-ignored fact that the calculated correlation values are only empirical estimates. To measure spurious behavior as deviation from a ground truth network, we simulate time-dependent isotropic random fields on the sphere and apply common network-construction techniques. We find several ways in which the uncertainty stemming from the estimation procedure has a major impact on network characteristics. When the data have a locally coherent correlation structure, spurious link bundle teleconnections and spurious high-degree clusters have to be expected. Anisotropic estimation variance can also induce severe biases into empirical networks. We validate our findings with ERA5 data. Moreover, we explain why commonly applied resampling procedures are inappropriate for significance evaluation and propose a statistically more meaningful ensemble construction framework. By communicating which difficulties arise in estimation from scarce data and by presenting which design decisions increase robustness, we hope to contribute to more reliable climate network construction in the future. Significance Statement Network-based approaches have gained renewed attention regarding the prediction of climate phenomena such as El Niño events, extreme regional precipitation patterns, anomalous polar vortex dynamics, and regarding understanding the Earth system. Even though climate networks are constructed from a limited amount of noisy data, they typically are not studied from a statistical perspective. However, such an approach is crucial: due to sampling uncertainty, climate networks unavoidably contain false and missing edges. We analyze how sampling artifacts impact the conclusions drawn from the networks and present both pitfalls and statistically robust procedures of network construction and evaluation. We aim to contribute to understanding the limitations and fully leveraging the potentials of network methods in climate and Earth system science.

Projected changes of precipitation extremes in North America using CMIP6 multi-climate model ensembles

In this study, we estimated a comprehensive set of extreme precipitation indices defined by the Expert Team on Climate Change Detection and Indices (ETCCDI) over North America (NA) using 18 selected higher-resolution GCMs of the sixth Coupled Model Intercomparison Project (CMIP6). The GCMs are evaluated in their historical simulations (1981–2010) against those estimated from in-situ (Daymet) and reanalysis (NARR) dataasets, followed by the projected changes in precipitation extremes of NA in response to climate warming (SSP1-2.6, SSP2-4.5 and SSP5-8.5) scenarios of CMIP6 in terms of temporal variations, spatial distributions, seasonal patterns and model agreement over the 21st century relative to 1981–2010. Results show that the CMIP6 ensemble median (CMIP6-EnM) can generally capture the observed extreme precipitation patterns of NA, and it out-performs all 18 individual GCMs with negative RMSE′XY, higher R statistics (R > 0.8) and the signal-to-noise ratio (SNR) > 1 in most sub-regions of NA. In general, extreme precipitation events are projected to occur more frequently and severely in the future, particularly in latitudes above 55°N of NA and in coastal areas. In contrast, dry conditions in southern NA (e.g., NCA and SCA) are projected to intensify over the 21st century. Seasonal changes are projected to be more significant in winter than in summer for northern NA (e.g., GIC, NWN and NEN), while in Central America (e.g., WNA, CAN, ENA), precipitation extremes will likely be less severe in both summer and winter. However, unlike strong model agreements in high (100%) and mid-latitudes (80%), there is often a wide disparity among the signs of projected changes between GCMs in southern NA. Unless greenhouse gas emissions are drastically reduced immediately, NA will likely be vulnerable to the impact of worsening extreme precipitation events especially in northern and coastal regions.

Variations of extreme precipitation events with sub-daily data: a case study in the Ganjiang River basin

Abstract. Climate warming increases the intensity of extreme precipitation. Studying extreme precipitation patterns and changes is vital to reducing risk. This paper investigates thresholds, changes and timescales for extreme precipitation using sub-daily records from meteorological stations in the Ganjiang River basin. We use the gamma distribution and select the L-moment method to estimate the parameters α and β. Results show that (1) continuous precipitation events of 36 h contributed the most precipitation to the total but with lower frequency, which would be key events for flood monitoring; (2) the intensity and the occasional probability of extreme precipitation will increase in spring in the future in stations like Yifeng, Zhangshu and Ningdu, which will in turn increase the risk of storm floods; and (3) spatial distribution of extreme precipitation risk shows that the risk increases as elevation increases in the northern lowland and the Jitai Basin in the midstream region, while the risk in the southern mountainous region decreases as elevation increases. These findings will facilitate emergency preparedness, including risk management and disaster assistance, in the study areas.

Spatial coherence patterns of extreme winter precipitation in the U.S.

Extreme precipitation events have a significant impact on life and property. The U.S. experiences huge economic losses due to severe floods caused by extreme precipitation. With the complex terrain of the region, it becomes increasingly important to understand the spatial variability of extreme precipitation to conduct a proper risk assessment of natural hazards such as floods. In this work, we use a complex network-based approach to identify distinct regions exhibiting spatially coherent precipitation patterns due to various underlying climate mechanisms. To quantify interactions between event series of different locations, we use a nonlinear similarity measure, called the edit-distance method, which considers not only the occurrence of the extreme events but also their intensity, while measuring similarity between two event series. Using network measures, namely, degree and betweenness centrality, we are able to identify the specific regions affected by the landfall of atmospheric rivers in addition to those where the extreme precipitation due to storm track activity is modulated by different mountain ranges such as the Rockies and the Appalachians. Our approach provides a comprehensive picture of the spatial patterns of extreme winter precipitation in the U.S. due to various climate processes despite its vast, complex topography.

Diverging identifications of extreme precipitation events from satellite observations and reanalysis products: A global perspective based on an object-tracking method

Extreme precipitation can trigger various natural hazards, causing catastrophic losses worldwide. As global warming accelerates, it is widely acknowledged that extreme precipitation will become more frequent and intense, calling for more accurate historical and projected precipitation estimation. Identifying extreme precipitation events is understudied particularly on the global scale, given that most studies only focus on pixel-by-pixel characteristics while ignoring the space-time continuity and evolution of extreme events. This study utilizes an object-based tracking method to track the precipitation system objects in time, extracting spatiotemporal attributes of precipitation events and investigates variable definitions of extremes for a better understanding of the performance of existing precipitation datasets. Five satellite and two reanalysis precipitation products (IMERG, GSMaP, PERSIANN-CCS, TRMM 3B42, CMORPH, ERA5, and ERA-Interim) are involved to investigate (1) the difference and similarity of those datasets in depicting the global pattern of extreme precipitation, (2) the impact of various definitions of extreme events, and (3) the trend of global extreme precipitation events. Results show that the object-based method can capture the motion of precipitation events. Different extreme definitions have a large impact on the distribution, trend, and features of extreme precipitation. The inter-comparison of extreme precipitation events from different products shows low correlation, indicating that accurately capturing the evolution of extreme events is still challenging. Reanalysis product-based analysis shows variable trends (magnitudes and signs) based on different attributes of precipitation objects and notable scale effect. Satellite precipitation products exhibit distinct inter-annual variability which could affect their hydrometeorological applications. In summary, this study leverages the object-based tracking method for comprehensive extreme precipitation analysis from the multi-dataset, multi-scale, and multi-definition perspectives, which can advance the understanding of the status and trend of global extreme precipitation.