Extreme Daily Rainfall Research Articles

Daily Rainfall Patterns During Storm “Daniel” Based on Different Satellite Data

Extreme rainfall from a long-lived weather system called storm “Daniel” occurred from 4th to 11th September 2023 over the central and eastern Mediterranean, leading to many devastating flood events mainly in central Greece and the western coastal parts of Libya. This study analyzes the daily rainfall amounts over all the affected geographical areas during storm “Daniel” by comparing three different satellite-based rainfall data products. Two of them are strictly related to Meteosat multispectral imagery, while the other one is based on the Global Precipitation Measurement (GPM) satellite mission. The satellite datasets depict extreme daily rainfall (up to 450 mm) for consecutive days in the same areas, with the spatial distribution of such rainfall amounts covering thousands of square kilometers almost during the whole period that the storm lasted. Moreover, the spatial extent of the heavy rainfall patterns was calculated on a daily basis. The convective nature of the rainfall, which was also recorded, characterizes the extremity of this weather system. Finally, the intercomparison of the datasets used highlights the satisfactory efficiency of the examined satellite datasets in capturing similar rainfall amounts in the same areas (daily mean error of 15 mm, mean absolute error of up to 35 mm and correlation coefficient ranging from 0.6 to 0.9 in most of the examined cases). This finding confirms the realistic detection and monitoring of the different satellite-based rainfall products, which should be used for early warning and decision-making regarding potential flood events.

Unravelling the complex interplay between daily and sub-daily rainfall extremes in different climates

Understanding short-duration intense rainfall is crucial for mitigating flash floods, landslides, soil erosion, and pollution incidents. Yet, most observations from rain gauges are only available at the daily resolution. We use the new Global Sub Daily Rainfall dataset to explore extreme rainfall at both daily and sub-daily durations worldwide. Employing Single Gauge Analysis (SGA) and pioneering global-scale Regional Frequency Analysis (RFA), we reveal for the first time how Generalized Extreme Value Distribution (GEV) parameters change across climates and data durations (1h, 3h, 6h, 24h, and daily). This marks the first-ever near-global-scale RFA, made possible by the development of an algorithm that automates RFA on observed rainfall datasets. We compare our results with GEV applied to a gridded rainfall reanalysis (ERA5). Our key findings are that: 1) using ERA5, return levels are significantly underestimated across all climates for 1h rainfall and across all data durations for gauges in the tropical climate region. Even when accounting for differences between point and areal estimates, the median 1h return level estimates are approximately 40% lower compared to RFA. We therefore strongly advise against the use of reanalysis gridded rainfall for studying these extremes. 2) While most gauges show similar return levels with RFA or SGA, some differ significantly, and either method may yield the highest values. Thus, we strongly recommend using both SGA and RFA simultaneously to estimate return levels for a robust risk assessment in flood infrastructure design. 3) The interaction between daily and sub-daily GEV shape parameters varies across climate regions, rendering a universal method for inferring sub-daily rainfall extremes from daily extremes (e.g., using Intensity-Duration-Frequency curves) impractical. Our research provides innovative methodological insights that warrant consideration in future studies on rainfall extremes. Our results not only benefit local stakeholders globally but also serve as a crucial validation tool for the rising number of convection-permitting climate model experiments conducted worldwide.

Analysis of Extreme Daily Rainfall in Cotonou, Benin (West Africa)

Global warming has a global impact on the water cycle and in particular on precipitation. This work is devoted to the analysis of the variability of extreme rainfall in the economic capital of Benin. For this purpose, the 58-year daily rainfall data collected at the Meteo Benin synoptic station in Cotonou between 1960 and 2018 are used. The annual daily maxima was established for the analysis. The generalized distribution of extreme values ​​(GEV) is used to estimate the quantiles according to the defined return periods. The daily heavy rainfall had a normal character with return periods of less than six years in Cotonou, between 1960 and 2018. There was a marginal increase in frequency of extreme rainfall since the beginning of 21st century. Extreme rainfall events may lead to floods as seen in 2010. Therefore, pro-active short- and long-term measures are the need of the hour to cope up against rainfall variability under the global warming scenario.

Geostatistical Simulation of Daily Rainfall Fields—Performance Assessment for Extremes in West Africa

Abstract The spatial description of high-resolution extreme daily rainfall fields is challenging because of the high spatial and temporal variability of rainfall, particularly in tropical regions due to the stochastic nature of convective rainfall. Geostatistical simulations offer a solution to this problem. In this study, a stochastic geostatistical simulation technique based on the spectral turning bands method is presented for modeling daily rainfall extremes in the data-scarce tropical Ouémé River basin (Benin). This technique uses meta-Gaussian frameworks built on Gaussian random fields, which are transformed into realistic rainfall fields using statistical transfer functions. The simulation framework can be conditioned on point observations and is computationally efficient in generating multiple ensembles of extreme rainfall fields. The results of tests and evaluations for multiple extremes demonstrate the effectiveness of the simulation framework in modeling more realistic rainfall fields and capturing their variability. It successfully reproduces the empirical cumulative distribution function of the observation samples and outperforms classical interpolation techniques like ordinary kriging in terms of spatial continuity and rainfall variability. The study also addresses the challenge of dealing with uncertainty in data-poor areas and proposes a novel approach for determining the spatial correlation structure even with low station density, resulting in a performance boost of 9.5% compared to traditional techniques. Additionally, we present a low-skill reference simulation method to facilitate a comprehensive comparison of the geostatistical simulation approaches. The simulations generated have the potential to provide valuable inputs for hydrological modeling.

Predictability of extreme southwest monsoon rainfall over Myanmar by using a physics-based empirical modeling approach

The study examines the ability to predict extreme rainfall days (ERDs) in Myanmar, a country frequently affected by severe flooding due to intense rainfall. We used a physics-based model combined with real-world data to forecast ERDs based on the atmospheric ERA5 reanalysis data and records rainfall from 79 different stations in Myanmar. The findings reveal that ERDs are more common in specific regions of Myanmar during certain seasons and are associated with particular atmospheric conditions. In June and July, El Niño's rapid growth phase—which was linked to a large anticyclone anomaly over the Indian Ocean—caused an increase in ERDs in central Myanmar. The region experiences higher rainfall as a result of this anomaly's enhanced moisture transfer. Similar to this, a zonal SLP dipole in eastern Myanmar contributes to higher ERDs from April to September. Additionally, the study finds indicators of possible causation, such as SST anomalies and the SIO dipole index, that are precursors to higher ERDs. Using these physical indicators, prediction models with strong TCC values for monsoon core region and northern Himalayas foothill region of Myanmar at different times demonstrated notable accuracy. The study finds a strong correlation between summer ERDs and monsoon rainfall, indicating that predicting mean rainfall accurately is essential for forecasting ERDs. To comprehend and forecast future changes in these relationships, more research is necessary due to the non-stationarity of predictor-predictand interactions. The discovery clarifies important atmospheric variables that impact extreme rainfall episodes in Myanmar and advances our understanding of these occurrences.

Future Scenarios of Design Rainfall Due to Upcoming Climate Changes in NSW, Australia

The occurrence of rainfall is significantly affected by climate change around the world. While in some places this is likely to result in increases in rainfall, both winter and summer rainfall in most parts of New South Wales (NSW), Australia are projected to decrease considerably due to climate change. This has the potential to impact on a range of hydraulic and hydrologic design considerations for water engineers, such as the design and construction of stormwater management systems. These systems are currently planned based on past extreme rain event data, and changes in extreme rainfall amounts due to climate change could lead to systems being seriously undersized (if extreme precipitation events become more common and/or higher in magnitude) or oversized (if extreme rainfall events become less frequent or decrease in magnitude). Both outcomes would have potentially serious consequences. Consequently, safe, efficient, and cost-effective urban drainage system design requires the consideration of impacts arising from climate change on the approximation of design rainfall. This study examines the impacts of climate change on the probability of occurrence of daily extreme rainfall in New South Wales (NSW), Australia. The analysis was performed for 29 selected meteorological stations located across NSW. Future design rainfall in this research was determined from the projected rainfall for different time periods (2020 to 2039, 2040 to 2059, 2060 to 2079, and 2080 to 2099). The results of this study show that design rainfall for the standard return periods was, in most cases, lower than that derived employing the design rainfall obtained from the Australian Bureau of Meteorology (BoM). While most of the analysed meteorological stations showed significantly different outcomes using the climate change scenario data, this varied considerably between stations and different time periods. This suggests that more work needs to be performed at the local scale to incorporate climate change predicted rainfall data into future stormwater system designs to ensure the best outcomes.

Projected Future Changes in Extreme Daily Rainfall in Southern Thailand Based on CIMP6 Models

Projected Future Changes in Extreme Daily Rainfall in Southern Thailand Based on CIMP6 Models

Synoptic and Mesoscale Dynamics of Cold Surges Over the South China Sea and Their Control on Extreme Rainfall

AbstractWe investigate the synoptic and mesoscale dynamics of two wet and two dry cold surges in January 2021 using a combination of observations, reanalysis, and convective‐scale model forecasts from the Met Office Unified Model (MetUM). We focus on the wet surges, and particularly the wettest days which are locally extreme over Singapore and the surrounding region (i.e., the daily mean and area‐averaged rainfall over 20 years exceeds the 99th percentile). On the large scale, the wet surges are characterized by an anomalously strong anticyclone over Siberia prior to their onset. The anticyclone and resultant surge winds are stronger than those of the dry surges. There is also a relatively moist (dry) environment prior to the onset of the wet (dry) surges, with the Madden‐Julian Oscillation (MJO) being in Phase 3 (Phase 6). On the mesoscale, the combination of the cold surge and a local tropical low produce strong, moist north‐easterly winds and convection over the Singapore region. The equatorward advection of positive anomalies of equivalent potential temperature resembles a weak gravity‐current‐like structure at its head, although the spatial scale is much too large for a gravity current. There is a moist bias in the model forecasts, although the precipitation is underestimated regionally during the wet surges and particularly on the extreme rainfall days. Overall, the model forecasts perform well synoptically but not in the details of mesoscale convection.

Modeling non-stationarity in extreme rainfall data and implications for climate adaptation: A case study from southern highlands region of Tanzania

The Southern Highlands region of Tanzania has witnessed an increased frequency of severe flash floods. This study examines rainfall data of four stations (Iringa, Mbeya, Rukwa, and Ruvuma) spanning 30 years (1991–2020) to investigate drivers of extreme rainfall and non-stationarity behavior. The Generalized Extreme Value (GEV) model, commonly used in hydrological studies, assumes constant distribution parameters, which may not be true due to climate variability, potentially leading to bias in extreme quantile estimation. Recent studies have introduced a technique for constructing non-stationary Intensity-Duration-Frequency (IDF) rainfall curves. The method incorporates trends in the parameters of the GEV distribution, only using time as a covariate. However, uncertainty exists about whether time is the most suitable covariate, highlighting the need to explore all potential covariates for modeling non-stationarity. The aim of this study is to assess the influence of other time-varying covariates on extreme daily rainfall events, considering seasonality and climate change in the rainfall data. Specifically, five processes (i.e., local temperature changes (LTC), urbanization, annual Global Temperature Anomaly (GTA), the Indian Ocean Dipole (IOD), and the El Niño-Southern Oscillation (ENSO) cycle) were studied as drivers of extreme rainfall events. Sixty two non-stationary GEV models are developed based on these covariates and their combinations, alongside two non-stationary GEV models using the time covariate to capture the seasonality of the unimodal rainfall in the region, and one stationary GEV model (S0). With the use of corrected Akaike Information Criterion (AICc), the best model for each duration (i.e., 1-, 3-, and 5-days) of rainfall series is chosen. Results indicate that local processes (i.e., LTC and urbanization) are the optimal covariates for 1 day-duration rainfall, while global processes (i.e, IOD, ENSO cycle, and GTA) are identified as the most suitable covariates for 3, and 5 day-duration rainfall across all stations. The identified best non-stationary model (with their best covariates) are then used to develop non-stationary rainfall IDF curves for all stations. According to the analysis of non-stationary extreme values, the return periods of extreme rainfall events concluded a notable decrease in comparison to the stationary approach. The study also revealed strong correlations between global climate indices (ENSO, IOD, GTA) and long-duration extreme rainfall in Tanzania’s Southern Highlands. Local factors like Urbanization and temperature changes also show significant associations with 1-day duration events. These findings emphasize the need for integrated climate forecasting to inform effective adaptation strategies. Finally, the study addresses associated uncertainties in our predictions of forthcoming extreme rainfall events through rigorous analysis. The study demonstrated that return levels for extreme rainfall events exhibit a rising trend with increasing return period, indicating heightened intensity over longer time spans, whereas, a relative uncertainty analysis illustrate escalating uncertainty with increasing return periods, emphasizing challenges in long-term prediction.

Comparison of Future Design Rainfall with Current Design Rainfall: A Case Study in New South Wales, Australia

Climate change impacts have the potential to alter the design rainfall estimates around the world. Decreasing trends in the summer and winter rainfall in New South Wales (NSW), Australia have already been observed due to climate variability and change. The derivation of design rainfall from historical rainfall, which is required for the design of stormwater management infrastructure, may be ineffective and costly. It is essential to consider climate change impacts in estimating design rainfall for the successful design of stormwater management infrastructure. In this study, the probability of the occurrence of daily extreme rainfall has been assessed under climate change conditions. The assessment was performed using data from 29 meteorological stations in NSW, Australia. For the evaluation of future design rainfall, the probability of the occurrence of extreme rainfall for different recurrence intervals was developed from daily extreme rainfall for the periods of 2020 to 2099 and compared with the current Australian Bureau of Meteorology (BoM) design rainfall estimates. The historical mean extreme rainfall across NSW varied from 37.71 mm to 147.3 mm, indicating the topographic and climatic influences on extreme rainfall. The outcomes of the study suggested that the future design rainfall will be significantly different from the current BoM estimates for most of the studied stations. The comparison of the results showed that future rainfall in NSW will change from −4.7% to +60% for a 100-year recurrence interval. However, for a 2-year recurrence interval, the potential design rainfall change varies from an approximately 8% increase to a 40% decrease. This study revealed that the currently designed stormwater management infrastructure will be idle in the changing climate.

Spatial prediction of changes in landslide susceptibility under extreme daily rainfall from the cmip6 multi-model ensemble

Spatial prediction of changes in landslide susceptibility under extreme daily rainfall from the cmip6 multi-model ensemble

Attributing Extreme Precipitation Characteristics in South China Pearl River Delta Region to Anthropogenic Influences Based on Pseudo Global Warming

AbstractIn the context of human‐induced warming climate, the atmosphere is expected to hold a greater amount of water vapor, leading to heavier precipitation on a global scale. However, the extent to which changes in extreme rainfall can be attributed to human influences varies at regional scales. Here we conduct attribution analyses on 40 extreme precipitation events in different seasons during 1998–2018 over the Pearl River Delta (PRD), by using the Weather Research and Forecasting (WRF) model and applying the pseudo global warming (PGW) method. The model was integrated with the factual and counterfactual conditions separately, with the latter derived from differences between the Coupled Model Intercomparison Project Phase 5 (CMIP5) historical and historical‐natural runs. By comparing parallel experiments, extreme daily rainfall (>95th percentile) in PRD enhanced by 8%–9.5% for 0.9–1.1 K near‐surface warming (nearly Clausius‐Clapeyron, or CC scaling) in the May‐to‐September (MJJAS) and 12.4% at most for 0.6–0.8 K warming (∼2 × CC rate) in non‐MJJAS seasons; the probability of those extremes increased by 10%–30% during MJJAS (20%–40% in other seasons). While moisture‐related thermodynamic effects play a similar role in modulating rainfall, the wind circulation‐related dynamic effects act differently in different seasons. Changes in MJJAS extremes are related to stronger low‐level southerly winds, while non‐MJJAS rainfall is exacerbated by strengthened low‐level wind convergence and updrafts. Moisture budget analysis suggests that thermodynamic effects associated with the increased moisture amount account for the mean rainfall increase, whereas dynamic effects related to wind circulation changes are responsible for extreme precipitation, regardless of seasons.

Sensitivity Analysis of Start Point of Extreme Daily Rainfall Using CRHUDA and Stochastic Models

Forecasting extreme precipitation is one of the basic actions of warning systems in Latin America and the Caribbean (LAC). With thousands of economic losses and severe damage caused by floods in urban areas, hydrometeorological monitoring is a priority in most countries in the LAC region. The monitoring of convective precipitation, cold fronts, and hurricane tracks are the most demanded technological developments for early warning systems in the region. However, predicting and forecasting the onset time of extreme precipitation is a subject of life-saving scientific research. Developed in 2019, the CRHUDA (Crossing HUmidity, Dew point, and Atmospheric pressure) model provides insight into the onset of precipitation from the Clausius–Clapeyron relationship. With access to a historical database of more than 600 storms, the CRHUDA model provides a prediction with a precision of six to eight hours in advance of storm onset. However, the calibration is complex given the addition of ARMA(p,q)-type models for real-time forecasting. This paper presents the calibration of the joint CRHUDA+ARMA(p,q) model. It is concluded that CRHUDA is significantly more suitable and relevant for the forecast of precipitation and a possible future development for an early warning system (EWS).

Interdecadal changes in the western Siberian summer mean and extreme rainfall during 1982–2021

Siberia, neighboring the Arctic Ocean, has a cold and dry climate. The famous Siberian forest, Taiga, has been greening in recent decades, mainly driven by climate factors. Besides the warming trend, summer rainfall is also critical for the vegetation variations there. This study finds that summer mean rainfall in western Siberia significantly increases after 1995 and decreases again slightly after 2012. The extreme rainfall shows similar interdecadal variations. Three periods are identified as 1982–1994 (P1), 1995–2011 (P2), and 2012–2021 (P3). The significant anomalous cyclone/anticyclone and water vapor convergence/divergence over western Siberia are responsible for the increased/decreased summer rainfall from P1 to P2/P2 to P3. All the extreme rainfall days show strong cyclonic anomalies in the lower level and a northwest–southeast-oriented anomalous cyclone–anticyclone dipole pattern in the upper level over western Siberia. The above anomalous circulation patterns are strongest during P2, when there are more extreme rainfall days with enhanced intensity. Distinct characteristics also exist in the developing processes of extreme rainfall events in the three periods.摘要本文研究发现, 西西伯利亚夏季降水在1995年后显著增加, 2012年后又有所减少, 极端降水日数和强度呈现一致的变化特征. 伴随这两次降水的年代际增加/减少, 西西伯利亚上空出现异常气旋/反气旋和水汽幅合/幅散. 极端降水发生时的区域环流特征在三个时段基本一致: 西西伯利亚低空出现气旋性异常, 高空为西北–东南向的异常气旋–反气旋偶极子型. 1995–2011年期间, 极端降水日数更多, 强度更强, 上述异常环流型也更强.

Domino: A new framework for the automated identification of weather event precursors, demonstrated for European extreme rainfall

AbstractA number of studies have investigated the large‐scale drivers and upstream precursors of extreme weather events, making it clear that the earliest warning signs of extreme events can be remote from the impacted region in both time and space. Integrating and leveraging our understanding of dynamical precursors provides a new perspective on ensemble forecasting for extreme events, focused on building storylines of possible event evolution. This then acts as a tool for raising awareness of the conditions conducive to high‐impact weather and providing early warning of their possible development. However, operational applications of this developing knowledge base are limited, perhaps for want of a clear framework for doing so. Here, we present such a framework, supported by open software tools, designed for identifying large‐scale precursors of categorical weather events in an automated fashion and reducing them to scalar indices suitable for statistical prediction, forecast interpretation, and model validation. We demonstrate this framework by systematically analysing the precursor circulations of daily rainfall extremes across 18 regional‐ to national‐scale European domains. We discuss the precursor rainfall dynamics for three disparate regions, and show our findings are consistent with, and extend, previous work. We provide an estimate of the predictive utility of these precursors across Europe based on logistic regression, and show that large‐scale precursors can usefully predict heavy rainfall between two and six days ahead, depending on region and season. We further show how, for more continental‐scale applications, the regionally specific precursors can be synthesised into a minimal set of indices that drive heavy precipitation. We then provide comments and guidance for generalisation and application of our demonstrated approach to new variables, timescales, and regions.

Probability Models and Some Mathematical Techniques on Parameter Estimation for Daily Rainfall Extremes: Application to Daily Rainfall in Southern Thailand

Probability Models and Some Mathematical Techniques on Parameter Estimation for Daily Rainfall Extremes: Application to Daily Rainfall in Southern Thailand

A Bayesian non-asymptotic extreme value model for daily rainfall data

Motivated by the analysis of extreme rainfall data, we introduce a general Bayesian hierarchical model for estimating the probability distribution of extreme values of intermittent random sequences, a common problem in geophysical and environmental science settings. The approach presented here is derived under a set of assumptions different than those used in traditional extreme value (EV) theory. A hierarchical model structure is adopted for capturing the underlying variability in the yearly distribution of event magnitudes and occurrences, which are described through a latent temporal process. Focusing on daily rainfall extremes, the structure of the proposed model lends itself to incorporating prior geo-physical understanding of the rainfall process. By means of an extensive simulation study, we show that this methodology can significantly reduce quantile estimation uncertainty with respect to Bayesian formulations of traditional asymptotic EV methods, particularly in the case of relatively small samples. On the other hand, a negative bias is observed in estimated rainfall quantiles. We evaluate the tradeoff between bias and variance for extreme rainfall quantiles and show that the approach proposed here can be advantageous depending on the sample size available for training. The benefits of the approach are illustrated with an application to a large data set of 479 long daily rainfall historical records from across the conterminous United States. By comparing measures of in-sample and out-of-sample predictive accuracy, we find that the model structure developed here, combined with the use of all available observations for inference, can improve robustness with respect to overfitting to the specific sample in case of short datasets.

The Potential Use of the SCS-CN Method to Estimate Extreme Floods in the West Bank Data-scarce Catchments

In the West Bank, extreme floods are becoming more frequent. This can be attributed to the expansion of urban areas together with the climate change impacts (the increasing rainfall intensity). This situation worsens given the lack of proper hydraulic infrastructures required to safely dispose of the accumulated floods. Thus, it becomes es-sential to estimate extreme flood values so as help decision-makers in formulating proper corrective and preventive strategies. This study utilizes the SCS-CN approach to estimate extreme flood values at the different West Bank data-scarce catchments. Maximum annual daily rainfall (MADR) values were used to obtain extreme rainfall daily grids for different selected return periods (10, 25, 50, and 100 years). The GIS was heavily used to apply the SCS-CN method for the entire West Bank catchments. Results show that extreme daily rainfall values are increasing with the increase of the return period. Further, catchments draining into the west (into the Mediterranean Sea) have more extreme daily rainfall values than those draining to the east (into the Dead Sea and the Jordan river) and as such, the expected extreme floods in these catchments are high for the selected return periods. Moreover, based on the average expected extreme flood values, a flood hazard map was developed that classifies West Bank catchments into 5 flood hazard zones. By area, about 42% of the total West Bank catchments are under high to very high flood hazard zone. The results of this research can be adapted to realize any administrative strategy changes for cohe-sive catchment-based extreme flood hazard management and to prioritize flood remedial plans at the different West Bank catchments

A gauge-based sub-daily extreme rainfall climatology for western Europe

We present here the first climatology of extreme sub-daily rainfall for a large part of western Europe, developed using a newly developed set of sub-daily rainfall indices, derived from a recently created global sub-daily rainfall (GSDR) dataset. The indices describe sub-daily (3-hourly) rainfall extremes in terms of their intensity, frequency and timing. Analysis of the frequency index (number of 3hr periods with >20 mm rain (R3hr20mm)) and a suite of intensity indices including the annual maxima (Rx3hr) and values of the 99.9th percentile at each gauge (R99.9p3hr) indicate a peak in the frequency and intensity of short-duration rainfall extremes in summer across most of western Europe, while areas around the north-west Mediterranean experience the highest intensities and frequencies of 3-hr rainfall in autumn. The index of contribution from 3-h annual maxima to the daily total (Rx3hrP) indicates that these events are often produced by very short-duration or very peaked storms, with a tendency towards a late afternoon or evening peak in the time of occurrence. There are also clear differences in the spatiotemporal occurrence of the sub-daily extremes when compared to extreme daily rainfall indices, which could have repercussions for flood management. Additional analysis of the sub-daily rainfall indices within the context of Köppen-Geiger climate zones indicates Mediterranean-type climate zones experience more intense and more frequent sub-daily extremes, while the intensity of rainfall within cooler climate zones is lower and the most intense events are restricted to summer. The climate zones are found to be a relatively good indicator of the extreme rainfall characteristics that can be expected in a region. Being able to compare sub-daily rainfall characteristics across a wide region in this manner greatly enhances our ability to investigate future variability and change in sub-daily extremes and will aid in high-resolution climate model validation.

Changes in the dynamical, thermodynamical and hydrometeor characteristics prior to extreme rainfall events along the southwest coast of India in recent decades

Extremes in rainfall have links with atmospheric dynamics, thermodynamics, and deep cloud properties. Employing station-based gridded rainfall data, analysis product of sea surface temperature (SST), reanalysis product of dynamic/thermodynamic parameters and remotely sensed cloud properties, we demonstrate the features and evolution of rainfall extremes over the southwest coast of India (8–14°N, 75–77.5°E) during monsoon season. Analysis of the frequency and total amount of extreme rainfall reveals that the northwest coast (14–20°N) typically serves as the central node for the precipitation extremes. However, linear trends in rainfall extremes are remarkably different across the northwest and southwest coasts of India. Specifically, we noted a marked decrease in extreme rain events between 14 and 16°N along the northwest coast, while at the same time, the southwest coast emerges as a hot spot for extreme rain events. Results suggest that the evolution of rainfall extremes on the southwest coast is linked to the warming of the south-central Arabian sea (AS), enhanced SST gradient between north and south AS, and low pressure over the Bay of Bengal (BoB) four days before the event day. Under the influence of these factors, moisture-laden winds shift from the northwest coast to the southwest coast of India. Three days before the extreme precipitation, the BoB low-pressure centre advances westward and eventually merges with the trough of low along the west coast on the event day and creates a considerable increase in the mid-tropospheric vorticity. Meteorological conditions on extreme rainfall days over the southwest coast are portrayed by a significant reduction of sea level pressure, an intense flow of low-level jets from the Arabian sea and a cyclonic vortex at mid-troposphere. These elements favour a substantial rise in moist static energy (MSE) throughout the troposphere, leading to deep convective clouds with copious ice content and extreme rainfall over the southwest coast.