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Nonstationary Rainfall frequency analysis (RFA) is used to assess how climate change is impacting the likelihood of extreme storms. A key limitation of covariate‐based approaches to nonstationary RFA is that without a physical basis, models selected based on the quality of fit to historical data cannot be reliably projected to estimate future quantiles. Here we propose to improve the physical representation of rainfall processes by using a peaks‐over‐threshold approach to separate the processes of storm intensity (impacted by thermodynamic drivers related to changes in atmospheric moisture) and storm arrival frequency (impacted by dynamic drivers that lead to changes in regional weather systems). Through stochastic experiments we demonstrate that quantiles can only be accurately projected beyond the observed climate when nonstationary models reflect the underlying nonstationary process. Through a case study we demonstrate how climate model projections of rainfall can be utilized to deduce nonstationary model structures, showing that changes in both the storm intensity and storm arrival frequency are needed to accurately estimate future quantiles. While here we propose a single simple physically informed approach for storm intensity, structuring the arrival frequency component requires a detailed understanding of atmospheric dynamics in the region of interest. Plain Language Summary We explore how complexity can be added to probability models to estimate how the risks of extreme rainfalls may change in a warming climate. We propose a way to structure these models to separate changes due to increased moisture holding capacity in the atmosphere due to warming temperatures (thermodynamics) from changes in atmospheric circulation patterns that affect weather systems (dynamics). This separation allows us to introduce physically motivated models which represent the impact of climate change on these two processes. We demonstrate the value of using this approach through several stochastic experiments which demonstrate that shifts in extreme rainfall probabilities can only be estimated when the underlying physical processes are correctly represented. A case study is provided to demonstrate how rainfall projections from a climate model can be used to develop physically motivated models for both thermodynamic and dynamic processes. Key Points Different nonstationary probability model structures are tested for their ability to estimate quantiles in future climates A peaks‐over‐threshold approach is utilized to separate thermodynamic and dynamic drivers of rainfall Physically motivated models are shown to be necessary to estimate extreme rainfall quantiles in a warming world

In China, the middle and lower reaches of the Yangtze River basin (MLRYRB) is a core region suffering frequent devastating floods triggered by heavy precipitation during warm seasons, exerting serious impacts on society. However, the physical mechanisms responsible for the increasing flood-inducing rainfall (FIR) frequency over MLRYRB during warm seasons remain unclear. Based on objective definition procedures, the present study investigates the salient atmospheric and oceanic signals tied to the interannual fluctuations of warm-season FIR frequency over MLRYRB. The results show that the suppressed convection from the remote western Pacific to the east of the Philippines could serve as a salient synchronous atmospheric signal for the increased FIR frequency. Moreover, the sea surface temperature (SST) warming over the tropical Indian Ocean (TIO) and the preceding wintertime El Niño-related SST anomaly pattern are deemed as salient contemporaneous and precursory oceanic signals linking the enhancement of the warm-season FIR frequency over MLRYRB on the interannual timescale, respectively. Further observational evidence and tropical Pacific pacemaker experiment results based on the Community Earth System Model Version 2 (CESM2) suggest that the mature El Niño in the prior winter can exert a delayed impact on the enhanced FIR frequency over MLRYRB during the subsequent warm season by exerting vital contributions to the FIR-favorable systems (i.e., southwestward-shifted western North Pacific anomalous anticyclone and the southward-displaced East Asian subtropical westerly jet). The basin-wide positive TIO SST anomalies act as El Niño’s capacitor to relay its impact. These signals have important implications for seasonal prediction of FIR frequency over MLRYRB, and it is essential to place a high requirement on consideration of the better-known El Niño’s cross-season atmospheric teleconnection.

This study examines the effects of five cumulus closure assumptions on simulations of summer precipitation in the continental U.S. by utilizing an ensemble cumulus parameterization (ECP) that incorporates multiple alternate closure schemes into a single cloud model formulation. Results demonstrate that closure algorithms significantly affect the summer mean, daily frequency and intensity, and diurnal variation of precipitation, with strong regional dependence. Overall, the vertical velocity (W) closure produces the smallest summer mean biases, while the moisture convergence (MC) closure most realistically reproduces daily variability. Both closures have advantages over others in simulating U.S. daily rainfall frequency distribution, though both slightly overestimate intense rain events. The MC closure is superior at capturing summer rainfall amount, daily variability, and heavy rainfall frequency over the Central U.S., but systematically produces wet biases over the North American Monsoon (NAM) region and Southeast U.S., which can be reduced by using the W closure. The instability tendency (TD) and the total instability adjustment (KF) closures are better at capturing observed diurnal signals over the Central U.S. and the NAM, respectively. The results reasonably explain the systematic behaviors of several major cumulus parameterizations. A preliminary experiment combining two optimal closures (averaged moisture convergence and vertical velocity) in the ECP scheme significantly reduced the wet (dry) biases over the Southeast U.S. in the summer of 1993 (2003), and greatly improved daily rainfall correlations over the NAM. Further improved model simulation skills may be achieved in the future if optimal closures and their appropriate weights can be derived at different time scales based on specific climate regimes.

Climate extreme events are becoming increasingly frequent worldwide, causing floods, drought, forest fires, landslides and heat or cold waves. Several studies have been developed on the assessment of trends in the occurrence of extreme events. However, most of these studies used traditional models, such as Poisson or negative binomial models. Thus, the main objective of this study is to use a space–time data counting approach in the modeling of the number of days with extreme precipitation as an alternative to the commonly used statistical methods. The study area is the Northeast Brazil region, and the analysis was carried out for the period between 1 January 1980 and 31 December 2010, by assessing the frequency of extreme precipitation represented by the R10 mm, R20 mm and R* indices.

The sub-seasonal variation of Indian summer monsoon rainfall highly impacts Kharif crop production in comparison with seasonal total rainfall. The rainfall frequency and intensity corresponding to various rainfall events are found to be highly related to crop production and therefore, the predictability of such events are considered to be diagnosed. Daily rainfall predictions are made available by one of the coupled dynamical model National Centers for Environmental Prediction Climate Forecast System (NCEPCFS). A large error in the simulation of daily rainfall sequence influences to take up a bias correction and for that reason, two approaches are used. The bias-corrected GCM is able to capture the inter-annual variability in rainfall events. Maximum prediction skill of frequency of less rainfall (LR) event is observed during the month of September and a similar result is also noticed for moderate rainfall event with maximum skill over the central parts of the country. On the other hand, the impact of rainfall weekly rainfall intensity is evaluated against the Kharif rice production. It is found that weekly rainfall intensity during July is having a significant impact on Kharif rice production, but the corresponding skill was found very low in GCM. The GCM are able to simulate the less and moderate rainfall frequency with significant skill.

After multiple simultaneous landslides caused by heavy rainfall, expanding landslides continue to occur for a certain duration. Evaluation of the influencing period of sediment yield due to expanding landslides is vital for comprehensive sediment management of the basin. In this study, we investigated a region with a low frequency of heavy rainfall that has not received its due level of attention until now. Consequently, the transition of expanding landslides depends on the transition of the number of remaining landslides, based on the difference in the frequency of heavy rainfall. Furthermore, the transition of expanding landslides depends on the maximum daily rainfall after the landslides. These findings indicate that “the number of remaining landslides” and “maximum daily rainfall after a landslide” are related factors that determine the period during which expanding landslides frequently occur. An estimation formula based on elapsed time was developed to calculate the number of remaining landslides. An empirical formula for the number of expanding landslides was obtained by multiplying the function of the daily maximum rainfall after the landslide by the estimation formula for the number of remaining landslides. The developed empirical formula can be used effectively for evaluation during periods when rainfall-induced landslides are subject to subsequent expansion.

Changes in the seasonality of precipitation over Africa have high potential for detrimental socioeconomic impacts due to high societal dependence upon seasonal rainfall. Here, for the first time we conduct a continental-scale analysis of changes in wet season characteristics under the RCP4.5 and RCP8.5 climate projection scenarios across an ensemble of CMIP5 models using an objective methodology to determine the onset and cessation of the wet season. A delay in the wet season over West Africa and the Sahel of over 5–10 days on average, and later onset of the wet season over southern Africa, is identified and associated with increasing strength of the Saharan heat low in late boreal summer and a northward shift in the position of the tropical rain belt over August–December. Over the Horn of Africa rainfall during the “short rains” season is projected to increase by over 100 mm on average by the end of the twenty-first century under the RCP8.5 scenario. Average rainfall per rainy day is projected to increase, while the number of rainy days in the wet season declines in regions of stable or declining rainfall (western and southern Africa) and remains constant in central Africa, where rainfall is projected to increase. Adaptation strategies should account for shorter wet seasons, increasing rainfall intensity, and decreasing rainfall frequency, which will have implications for crop yields and surface water supplies.

Changes in the frequency and intensity of rainfall are an important potential impact of climate change. Two modes of change, a shift and an increase, are applied to simulations of global warming with models from phase 5 of the Coupled Model Intercomparison Project (CMIP5). The response to CO₂ doubling in the multimodel mean of CMIP5 daily rainfall is characterized by an increase of 1% K–1at all rain rates and a shift to higher rain rates of 3.3% K–1. In addition to these increase and shift modes of change, some models also show a substantial increase in rainfall at the highest rain rates called the extreme mode of response to warming. In some models, this extreme mode can be shown to be associated with increases in grid-scale condensation or gridpoint storms.

The accurate prediction of rainfall, and in particular of the heaviest rainfall events, remains challenging for numerical weather prediction (NWP) models. This may be due to subgrid‐scale parameterizations of processes that play a crucial role in the multi‐scale dynamics generating rainfall, as well as the strongly intermittent nature and the highly skewed, non‐Gaussian distribution of rainfall. Here we show that a U‐Net‐based deep neural network can learn heavy rainfall events from a NWP ensemble. A frequency‐based weighting of the loss function is proposed to enable the learning of heavy rainfall events in the distributions' tails. We apply our framework in a post‐processing step to correct for errors in the model‐predicted rainfall. Our method yields a much more accurate representation of relative rainfall frequencies and improves the forecast skill of heavy rainfall events by factors ranging from two to above six, depending on the event magnitude. Plain Language Summary Modeling rainfall is challenging because of its large variability in space and time, and its highly skewed distribution. Numerical weather prediction (NWP) models have to be simulated on discretized grids with finite resolution. Although important especially for the generation of rainfall, small‐scale processes can therefore not be resolved explicitly and must be paremeterized, that is, included as empirical functions of the resolved variables. This introduces model biases that can lead to an under‐ or overestimation of heavy rainfall events. Here we apply a deep neural network (DNN) to correct biases in the rainfall forecast of a NWP ensemble. The DNN is optimized with a loss function that includes weights to account for heavy rainfall events, and shows substantially improved performance in their prediction. Key Points Correcting biases in the rainfall forecast of a numerical weather prediction ensemble with a deep neural network Training with a weighted loss function combining two terms enables the neural network to learn the heavy tailed target distribution The method improves the relative frequency and categorical skill scores of heavy rainfall

The impact of climate change on tropical cyclones (TCs) is of great concern in the Western North Pacific (WNP) region. Observations suggest that there have been recent changes in TC-related heavy rainfall. However, it has not yet been determined whether anthropogenic forcing has any contribution to such changes. Here, we show evidence that the human-induced warming has considerably changed the frequency of TC-induced heavy rainfall events in the WNP region. Observations since 1961 show that the occurrence of TC-induced heavy rainfall has significantly increased along coastal East Asia, while it has decreased in the southern part of WNP. On the basis of large ensemble climate simulations, we demonstrate that the observed changes cannot be explained solely by natural variability. This suggests that anthropogenic impacts have already significantly altered the TC-induced heavy rainfall pattern in the WNP region.The coastal regions of the Western North Pacific have seen large increases in tropical cyclone heavy rainfall frequency. Statistical fingerprint analysis shows that this observed geographical change in heavy rainfall is related to anthropogenic climate change.