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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 events Rainfall impact of multi‐year El Niño‐Southern Oscillation (ENSO) persists during double and triple events, despite no strengthening of ENSO Australian rainfall increases in the third year of triple La Niña likely due to soil moisture‐rainfall feedback

Flood risk analyses often focus on a single flooding source, typically storm surge or rainfall‐driven flooding, depending on the predominant threat. However, hurricanes frequently cause compound flooding through significant storm surges accompanied by heavy rainfall. This study employs a hydrodynamic model based on Delft3D‐Flexible Mesh that couples flow, waves, and rainfall‐driven flow to simulate five historical tropical cyclones in Virginia's southeast coastal region. These storms produced varying intensities of storm surge and rainfall in the study area. Model simulations, incorporating rainfall through a rain‐on‐grid approach, account for the dynamic interaction between storm tides, and pluvial flow and enable the definition of flood zones as hydrologic, transitional, and coastal zones. This compound flooding model was validated with water level data from in‐water and overland gauges. The results indicate that the magnitude of the coastal zone correlates strongly with the extent of the surge‐inundated area (SIA) obtained from simulations that only considered storm surges. The extent of the transitional zone correlates strongly with the product of SIA and total rainfall. As an additional measure for flood hazards besides water depth, we calculated flow momentum flux at different flood zones to assess potential damage from hydrodynamic loads on structures, vehicles, and pedestrians. A strong correlation was found between the magnitude of the surge and momentum flux. Furthermore, high rainfall rates and winds can cause a significant increase in momentum flux locally. Understanding flood zones and their flow dynamics helps to identify effective flood risk management strategies that address the dominant flood driver. Plain Language Summary Flood risk analyses in coastal areas usually study storm surges and rainfall impacts separately. However, hurricanes often cause compound flooding which stems from both sources. This research studies compound flooding using a computational model to simulate five hurricanes that hit coastal Virginia and had a range of surge and rainfall intensities. We identified three flood zones: areas flooded dominantly by rainfall (hydrologic), areas where both surge and rainfall contribute to flooding (transitional), and areas dominated by storm surge (coastal). The extent of the coastal zone correlated strongly with the magnitude of storm surge, and the extent of the transitional zone correlated very strongly with the area inundated by storm surge multiplied by total rainfall. Additionally, we investigated flow momentum, as a measure of flood force on objects. While a large surge causes a large flow momentum, heavy rain and strong winds can create energetic flows in the hydrologic zone too. Analyzing flood zones and flow momentum helps to identify proper flood mitigation measures and quantify their efficiency. For example, flood gates or levees are suitable for coastal zones, and improvements in drainage systems and inland green infrastructure are suitable for hydrologic zones, while a combination suits transitional zones. Key Points A hydrodynamic model for compound flooding is used to define hydrologic, transitional, and coastal zones at city and neighborhood scales The area of the transitional zone correlates very strongly with the inundated area in storm surge simulations multiplied by total rainfall Flow momentum flux, as a measure of flood force, is influenced by surge magnitude, wind, and rainfall intensity

Although the El Niño-Southern Oscillation (ENSO) is the leading mode of climate variability in southern Africa during the austral summer season, the impacts are nonlinear and not all events result in the expected impact. Limited work has been carried out to explore the role ENSO diversity plays in southern African climate, which this study aims to address. Here, the influence of El Niño diversity on rainfall characteristics and whether the impact evolves on sub-seasonal scales are examined. Two broad types of El Niño events, namely Eastern Pacific (EP) and Central Pacific (EP) events, are first determined by the location of the positive SST anomaly in the equatorial Pacific. For the 1950–2022 period, 9 EP El Niño events and 10 CP El Niño events are identified. Results show significant variability in ENSO impacts on a sub-seasonal scale across southern Africa during the summer half of the year (October-March). EP events affect rainfall throughout the summer, with the strongest impacts in the core months (Dec-Jan), characterized by less rainfall, more frequent dry spells and extended dry periods. EP events have a stronger relationship with various rainfall characteristics across most of southern Africa compared to CP events. Consequently, the likelihood of experiencing a significant summer rainfall deficit is higher during EP events. These findings indicate that traditional seasonal definitions, like JFM, or generalizing ENSO as a single type of event, may be inadequate in assessing ENSO-induced rainfall impacts from a seasonal forecasting perspective.

With ongoing global warming, the amount and frequency of precipitation in the tropics is projected to change substantially. While it has been shown that tropical forests and savannahs are sustained within the same intermediate mean annual precipitation range, the mechanisms that lead to the resilience of these ecosystems are still not fully understood. In particular, the long-term impact of rainfall variability on resilience is as yet unclear. Here we present observational evidence that both tropical forest and savannah exposed to a higher rainfall variability—in particular on interannual scales—during their long-term past are overall more resilient against climatic disturbances. Based on precipitation and tree cover data in the Brazilian Amazon basin, we constructed potential landscapes that enable us to systematically measure the resilience of the different ecosystems. Additionally, we infer that shifts from forest to savannah due to decreasing precipitation in the future are more likely to occur in regions with a precursory lower rainfall variability. Long-term rainfall variability thus needs to be taken into account in resilience analyses and projections of vegetation response to climate change.Tropical forests and savannah are more resilient to climate disturbances when they have been exposed to higher rainfall variability in the long-term past, finds an analysis of Brazilian rainfall and tree-cover observations.

El Niño‐Southern Oscillation (ENSO), where the central‐east tropical Pacific is unusually warm (El Niño) or cold (La Niña), is known to influence Australian rainfall. Here, we detail the extent of ENSO's influence on Australian monthly rainfall distributions and clarify its many complexities. We show La Niña to be a long‐lasting and wide‐spread intensifier of Australian rainfall throughout its lifecycle, particularly on extreme monthly rainfall. The reduction of rainfall during El Niño is comparatively limited; confined mainly to El Niño's developing phase and the southeast and northeast of Australia. A further complexity shows El Niño can intensify monthly rainfall during its mature phase. Within these broader impacts of ENSO are strong spatial and temporal differences, such that the expected rainfall impacts may not be consistently felt at the local‐scale. We propose methods to account for these complex climatic impacts at scales comparable to a river catchment scale.

Strong positive Indian Ocean Dipole (pIOD) events like those in 1997 and 2019 caused significant flooding in East Africa. While future projections indicate an increase in pIOD events, limited historical data hinders a comprehensive understanding of these extremes, particularly for unprecedented events. To overcome this we utilize a large ensemble of seasonal reforecast simulations, which show that regional rainfall continues to increase with pIOD magnitude, with no apparent limit. In particular we find that extreme rain days are highly sensitive to the pIOD index and their seasonal frequency increases super-linearly with higher pIOD magnitudes. It is vital that socio-economic systems and infrastructure are able to handle not only the increasing frequency of events like 1997 and 2019 but also unprecedented seasons of extreme rainfall driven by as-yet-unseen pIOD events. Future studies should prioritize understanding the hydrological implications and population exposure to these unprecedented extremes in East Africa.

The El Niño‐Southern Oscillation (ENSO) importantly influences Australian rainfall variability. In southeastern Australia, La Niña is observed to cause a larger risk of high rainfall than El Niño does for low rainfall, demonstrating a non‐linear ENSO‐rainfall relationship. In this study, we first assess the ENSO‐rainfall relationship for 10 single model initial‐condition large ensembles (LEs) over the Murray‐Darling Basin. We then apply a novel Fraction of Attributable Risk framework to quantify the projected rainfall risk in this region, specifically to the five LEs that have the same ENSO‐rainfall relationship as observations. Despite no prominent change in El Niño related rainfall amount or its variability, rainfall variability increases during La Niña and neutral phases. Additionally, three out of five LEs project an increase in rainfall risk during La Niña events, with both greater variability and higher amounts of rainfall, suggesting that the La Niña‐driven rainfall impacts may worsen under a warming climate.

The majority of flood assessment and warning systems primarily focus on the occurrence of floods caused by river overflow, taking into account factors such as intense precipitation. Improving flood resilience, on the other hand, requires a deeper understanding of how these factors affect each other and how specific local conditions can have an impact. This study offers impartial tools for estimating the severity of the effects brought on by heavy rainfall to facilitate the prompt communication of effective measures, such as the evacuation of livestock and human settlements and the provision of medical assistance. These tools take into account the cascading effects of various causative factors contributing to heavy rainfall. This article aims to assess the various factors that contribute to the impacts of heavy rainfall, including the timestamp (indicating soil saturation and moisture levels), river gauges (determining water congestion in canal systems), average aerial precipitation (indicating runoff), and the rainfall itself, taking into account both in situ and ex-situ impacts. Support Vector Machine (SVM), Decision Tree (DT), K-Nearest Neighbour (KNN), and Naive Bayes are some of the machine learning methods used in the study to find out how dynamically vulnerable affected districts are to flooding in different compound scenarios. This analysis is conducted by leveraging historical observed datasets. The results demonstrate the feasibility of mitigating the issue of excessive and insufficient flood warnings resulting from the cumulative effects of intense precipitation. By implementing a categorization system that divides the affected areas into various portions, or districts, according to the main factors contributing to flooding, namely rainfall, river discharge, and runoff, The suggested model presents novel insights into the sequential consequences of intense precipitation in the regularly inundated regions of North Bihar, India. Innovative tools can serve as valuable resources for flood forecasters and catastrophe managers to comprehend the extent of flooding and the consequential effects of intense precipitation.

Investigating the relationships between climate extremes and crop yield can help us understand how unfavourable climatic conditions affect crop production. In this study, two statistical models, multiple linear regression and random forest, were used to identify rainfall extremes indices affecting wheat yield in three different regions of the New South Wales wheat belt. The results show that the random forest model explained 41–67% of the year-to-year yield variation, whereas the multiple linear regression model explained 34–58%. In the two models, 3-month timescale standardized precipitation index of Jun.–Aug. (SPIJJA), Sep.–Nov. (SPISON), and consecutive dry days (CDDs) were identified as the three most important indices which can explain yield variability for most of the wheat belt. Our results indicated that the inter-annual variability of rainfall in winter and spring was largely responsible for wheat yield variation, and pre-growing season rainfall played a secondary role. Frequent shortages of rainfall posed a greater threat to crop growth than excessive rainfall in eastern Australia. We concluded that the comparison between multiple linear regression and machine learning algorithm proposed in the present study would be useful to provide robust prediction of yields and new insights of the effects of various rainfall extremes, when suitable climate and yield datasets are available.

One of the key parameters constituting the basis for the operational assessment of stormwater systems is the annual number of storm overflows. Since uncontrolled overflows are a source of pollution washed away from the surface of the catchment area, which leads to imbalanced receiving waters, there is a need for their prognosis and potential reduction. The paper presents a probabilistic model for simulating the annual number of storm overflows. In this model, an innovative solution is to use the logistic regression method to analyze the impact of rainfall genesis on the functioning of a storm overflow (OV) in the example of a catchment located in the city of Kielce (central Poland). The developed model consists of two independent elements. The first element of the model is a synthetic precipitation generator, in which the simulation of rainfall takes into account its genesis resulting from various processes and phenomena occurring in the troposphere. This approach makes it possible to account for the stochastic nature of rainfall in relation to the annual number of events. The second element is the model of logistic regression, which can be used to model the storm overflow resulting from the occurrence of a single rainfall event. The paper confirmed that storm overflow can be modeled based on data on the total rainfall and its duration. An alternative approach was also proposed, providing the possibility of predicting storm overflow only based on the average rainfall intensity. Substantial simplification in the simulation of the phenomenon under study was achieved compared with the works published in this area to date. It is worth noting that the coefficients determined in the logit models have a physical interpretation, and the universal character of these models facilitates their easy adaptation to other examined catchment areas. The calculations made in the paper using the example of the examined catchment allowed for an assessment of the influence of rainfall characteristics (depth, intensity, and duration) of different genesis on the probability of storm overflow. Based on the obtained results, the range of the variability of the average rainfall intensity, which determines the storm overflow, and the annual number of overflows resulting from the occurrence of rain of different genesis were defined. The results are suited for the implementation in the assessment of storm overflows only based on the genetic type of rainfall. The results may be used to develop warning systems in which information about the predicted rainfall genesis is an element of the assessment of the rainwater system and its facilities. This approach is an original solution that has not yet been considered by other researchers. On the other hand, it represents an important simplification and an opportunity to reduce the amount of data to be measured.