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The annual maximum rainfall event (AMRE) refers to the maximum consecutive five-day rainfall in a year. In North China, these events account for 15%–80% of the total summer (June–August) rainfall amount and pose a great challenge for subseasonal-to-seasonal forecasting. Based on data analyses during 1979–2023, this study shows the interannual variability of AMRE is significantly influenced by the phase and amplitude mode of the annual cycle of the East Asian summer monsoon (EASM), characterized by two orthogonal patterns of southeasterly winds at 850 hPa over the northwestern Pacific. The EASM phase-locked AMRE shows heavy rainfall events occurring extremely early and late in Beijing and surrounding areas, corresponding to the peak southeasterly wind anomalies in June and August. The EASM amplitude-locked AMRE exhibits extreme heavy or light rainfall over southwest areas with normal phase. Therefore, AMRE has a potential predictability on the seasonal time scale due to its phase- and amplitude-locking with the slow variation of the annual cycle of the EASM.

Maximum daily rainfalls and their frequency are of great interest to design engineers and water resource decision makers especially for urban hydrology tasks, civil engineering structures, and protection against floods. When rainfall data is unavailable, estimating the return period rainfall becomes a complicated task. The main purpose of the present study is to estimate the maximum rainfalls for any return period starting from the mean interannual rainfall at any location over the whole Algerian territory. The analysis of rainfall data has shown a good correlation between the mean interannual rainfall and the maximum daily one, which allows deriving a simple formula of the relationship between these two parameters. The formula was used to estimate the maximum rainfall for any return period using the statistical approach. It was found that the errors follow the normal distribution. The maximum errors do not exceed 15% for 100 years return period. The developed approach is very useful for ungauged basins, where estimating extreme rainfalls can be complicated, and can give acceptable maximum rainfall values to be used in flood forecasting or structure design.

Hydrologists need to predict extreme hydrological and meteorological events for design purposes, whose magnitude and probability are estimated using a probability distribution function (PDF). The choice of an appropriate PDF is crucial in describing the behavior of the phenomenon and the predictions can differ significantly depending on the PDF. So, the success of the probability distribution function in representing the data of extreme value series of natural events such as hydrology and climatology is of great importance. Depending on whether the series consists of maximum or minimum values, the theoretical probability density function must be appropriately fit to the right or left tail of the extreme data, which contains the most critical information. This study includes a combined evaluation of the performance of four different tests for selecting the appropriate probability distribution of maximum rainfall in Türkiye: Kolmogorov–Smirnov (KS) test, Anderson–Darling (AD) test, Probability Plot Correlation Coefficient (PPCC) test, and L-Moments ZDIST test. Within the scope of the study, maximum rainfall series of seven rainfall durations from 15 to 1440 min, at rain gauge stations in 81 provinces of Türkiye, were examined. Goodness of fit was performed based on ranking using a combination of four different numerical tests (KS, AD, PPCC, ZDIST). The probabilistic character of maximum rainfall was evaluated using a large dataset consisting of 567 time series with record lengths ranging from 45 to 80 years. The goodness of fit of distributions was examined from three different perspectives. The first is an examination considering rainfall durations, the second is a province-based examination, and the third is a general country-based assessment. In all three different perspectives, the Wakeby distribution was determined as the best fit candidate to represent the maximum rainfall in Türkiye.

Severe water erosion occurs during extreme storm events. Such an exceedingly severe storm occurred in Zhengzhou in central China on 20 July 2021 (the 7.20 storm). The magnitude and frequency of occurrence of this storm event were examined in terms of how erosive it was. To contextualize this extreme event, hourly rainfall data from 2420 automatic meteorological stations in China from 1951 to 2021 were analyzed to (1) characterize the spatial and temporal distribution of the rainfall amount and rainfall erosivity of the 7.20 storm, (2) evaluate the average recurrence interval of the maximum daily and event rainfall erosivity, and (3) establish the geographical distribution of the maximum daily and event rainfall erosivity in China. The center of the 7.20 storm moved from southeast to northwest in Henan Province, and the most intense period of rainfall occurred in the middle and late stages of the storm. Zhengzhou Meteorological Station happened to be aligned with the center of the storm, with a maximum daily rainfall of 552.5 mm and a maximum hourly rainfall intensity of 201.9 mm h−1. The average recurrence intervals of the maximum daily rainfall erosivity (43 354±1863 MJ mm ha−1 h−1) and the maximum event rainfall erosivity (58 874±2351 MJ mm ha−1 h−1) were estimated to be about 19 200 and 53 700 years, respectively, assuming the log-Pearson type-III distribution, and these were the maximum rainfall erosivities ever recorded among 2420 meteorological stations in mainland China up to 2022. The 7.20 storm suggests that the most erosive of storms does not necessarily occur in the wettest places in southern China, and these can occur in mid-latitude around 35∘ N with a moderate mean annual rainfall of 566.7 mm in Zhengzhou.

In this work, the capability of STORAGE (STOchastic RAinfall GEnerator) model for generating long and continuous rainfall series for the upper Vistula basin (southern Poland) is tested. Specifically, in the selected area, only parameters of depth–duration–frequency curves are known for sub-daily rainfall heights (which are usually estimated in an indirect way by using Lambor's equations from daily data), while continuous daily series with a sufficient sample size are available. Attention is focused on modelling the sample frequency distributions of daily annual maximum rainfall. The obtained results are promising for further elaborations, concerning the use of STORAGE synthetic continuous rainfall data as input for a continuous rainfall-runoff approach, to be preferred with respect to classical event-based modelling.

This study assesses the frequency and intensity of rainfall and determines the optimal methods for estimating extreme rainfall in the Itacaiúnas River watershed (IRW), situated in the eastern Brazilian Amazon. Daily rainfall data from 1988 to 2018 were acquired from the Brazilian National Water Agency (ANA) and the National Institute of Meteorology (INMET), whereas hourly data from 2016 to 2018 were obtained from the Vale Institute of Technology and INMET. To fit the annual maximum daily rainfall data, we employed 11 probability distribution functions (PDFs) and evaluated their efficacy using the Kolmogorov-Smirnov (KS) and Anderson-Darling (AD) tests, as well as the Akaike information criterion (AIC), Bayesian information criterion (BIC), and log-likelihood function (LLF). The Gumbel and gamma distributions yielded superior results, as evidenced by the AIC and BIC criteria. The LLF demonstrated that the GEV and Weibull 3 PDFs better fit the maximum annual rainfall series. We calculated rainfall disaggregation coefficients from the rainfall schedule data to estimate maximum rainfall for different duration periods. A comparison with CETESB coefficients revealed that updating these estimates is necessary for accurately representing intense rainfall events in the eastern Amazon. Our analysis estimated upper hourly maximum rainfall of up to 115 mm/h for a return period of 100 years.

This study aims to investigate the trend behavior of monthly maximum in daily rainfall categories in the Vu Gia-Thu Bon river basin located in central Vietnam. Daily maximum rainfall series from 12 rainfall stations for the period 1979–2018 were utilized to characterize six categories of the intensity of daily maximum rainfall: light (0–4 mm/day, category A), mild-moderate (4–16 mm/day, category B), moderate-heavy (16–32 mm/day, category C 1 ), heavy (32–64 mm/day, category C 2 ), heavy-torrential (64–128 mm/day, category D 1 ), and torrential (≥ 128 mm/day, category D 2 ). The new approach of the Innovative Trends Analysis was then applied to the six classified categories. The results revealed that category B had a dominant increasing trend (32% of rain events) for all the stations in January (5.85%) and February (3.44%). In March and April, category A was dominant with 45% and 20%, respectively. In July, category C 1 was dominant with 25%, while in August and September, category C 2 prevailed over all stations with 45% (all stations) and 20%, respectively. The categories D 1 and D 2 were observed at all stations in December and November, with 26% and 31% of events, respectively. These results indicate an increasing trend in the categories B, C 1 , C 2 , and D 1 .

The tropical cyclones (TCs) formed over the North Indian Ocean (NIO) have paramount socio-economic impacts over India and neighbouring countries due to heavy rainfall, strong wind and high storm surge. In this study, mean rainfall characteristics of three different intensity stages of TCs over the NIO have been examined using a merged satellite-gauge daily rainfall product to better TC rainfall prediction over the region. A total of 32 TCs with 160 days of rainfall over the NIO between October 2015 and December 2021 have been considered. The mean positions of TCs formed over the AS are more than 450 km west during the post-monsoon season than the pre-monsoon season. During the post-monsoon TCs, mean translational speed increases with increase in TC intensity over the Bay of Bengal (BoB), while TCs in the Arabian Sea (AS) move rather slower with increase in intensity. Heavy TC rainfall areas have seen to be larger during the pre-monsoon season than the post-monsoon season over both BoB and AS basins. It is due to smaller mean sea level pressure, and stronger lower and middle level winds over both basins of the NIO during the pre-monsoon season as compared to the post-monsoon season. However, intensity of mean rainfall is higher for the TCs over the AS than the BoB during the pre-monsoon season. Heavy rainfall radius is maximum for depression and deep depression stages of TCs during the pre-monsoon season, while it is maximum for cyclonic storm and severe cyclonic storm stages of TCs during the post-monsoon season over both basins of the NIO. The largest heavy rainfall radius of about 650 km in the northeast geographical quadrant is observed for the pre-monsoon depression and deep depression stages of TCs over the BoB basin. The most intense daily rainfall occurs during the pre-monsoon TCs and the least intense daily rainfall occurs during the post-monsoon TCs over the AS basin. A consistent increase in daily maximum rainfall and decrease in its distance from the TC centre with the increase in TC intensity are observed over the AS for both seasons. The distance of maximum daily rainfall grid from the TC centre is the largest of about 355 km for depression and deep depression stages of TCs formed over the AS during both pre-monsoon and post-monsoon seasons. Furthermore, results indicate that translational speed of TCs has no impact on daily maximum rainfall over the BoB, whereas daily maximum rainfall shows statistically significant negative correlation with the TC translational speed over the AS. This study will be very useful for better TC rainfall forecasting over the NIO region.

A significant amount of information on regional rainfall characteristics is available nowadays, allowing its use in hydrological applications. This article is motivated by the availability of regional studies regarding maximum daily rainfall and intensity–duration–frequency curves that can be coupled with the design storm concept for urban hydrology studies. This is accomplished through a convenient index describing temporal variability of rainfall. More precisely, a methodology for regionalising the two parameters (i0, φ) of the two-parameter gamma design storm (G2P) is developed herein. A three-step methodology is proposed for obtaining the two parameters (i0, φ) for a given location. The results obtained in a case study show coherence with previous studies concerning maximum rainfall statistics.

Rainfall erosivity is defined as the rainfall potential to cause erosion. Its concept is based on the kinetic energy of rainfall, rainfall intensity, and maximum rainfall intensity in a 30-min period, and purposes recurrence analyzes involving soil losses. It is a climatic index related to damages caused by erosion, landslides, and flooding. This study sought to: (1) model daily rainfall erosivity in Mantiqueira Range Region (MRR), Southeastern Brazil; and (2) propose the Rmaxday as an indicator of the areas prone to natural disasters. Rmaxday is defined as the maximum daily rainfall erosivity and is determined from the maximum daily rainfall. It should be calculated from a historical series over the least 22 years. Three seasonal models were fitted using observed historical series. The models exhibited consistent statistical performances (CNS = 0.55, on average), thereby indicating that they can be used for further studies regarding natural disasters in the MRR. The Center to Northeastern MRR had the most vulnerable areas, as they experienced Rmaxday values > 1600 MJ ha−1 mm h−1 in the year of 2000 when fatalities were registered. Overall, the first half of January had the greatest Rmaxday in MRR. This period has been the most relevant for provoking natural disasters caused by heavy rainfall in MRR. Rmaxday is a promising indicator to identify areas prone to natural disasters, as it is more robust than the commonly used rainfall depth intervals, i.e., there is a relationship between its magnitude and the damages provoked by natural disasters.