Deep Learning Techniques in Extreme Weather Events: A Review

Extreme weather events pose significant challenges, thereby demanding techniques for accurate analysis and precise forecasting to mitigate its impact. In recent years, deep learning techniques have emerged as a promising approach for weather forecasting and understanding the dynamics of extreme weather events. This review aims to provide a comprehensive overview of the state-of-the-art deep learning in the field. We explore the utilization of deep learning architectures, across various aspects of weather prediction such as thunderstorm, lightning, precipitation, drought, heatwave, cold waves and tropical cyclones. We highlight the potential of deep learning, such as its ability to capture complex patterns and non-linear relationships. Additionally, we discuss the limitations of current approaches and highlight future directions for advancements in the field of meteorology. The insights gained from this systematic review are crucial for the scientific community to make informed decisions and mitigate the impacts of extreme weather events

Hurricane Forecasting: A Novel Multimodal Machine Learning Framework

This paper describes a machine learning (ML) framework for tropical cyclone intensity and track forecasting, combining multiple distinct ML techniques and utilizing diverse data sources. Our framework, which we refer to as Hurricast (HURR), is built upon the combination of distinct data processing techniques using gradient-boosted trees and novel encoder-decoder architectures, including CNN, GRU and Transformers components. We propose a deep-feature extractor methodology to mix spatial-temporal data with statistical data efficiently. Our multimodal framework unleashes the potential of making forecasts based on a wide range of data sources, including historical storm data, and visual data such as reanalysis atmospheric images. We evaluate our models with current operational forecasts in North Atlantic and Eastern Pacific basins on 2016-2019 for 24-hour lead time, and show our models consistently outperform statistical-dynamical models and compete with the best dynamical models, while computing forecasts in seconds. Furthermore, the inclusion of Hurricast into an operational forecast consensus model leads to a significant improvement of 5% - 15% over NHC's official forecast, thus highlighting the complementary properties with existing approaches. In summary, our work demonstrates that combining different data sources and distinct machine learning methodologies can lead to superior tropical cyclone forecasting. We hope that this work opens the door for further use of machine learning in meteorological forecasting.Comment: Under revision by the AMS' Weather and Forecasting journa

Modern Climatology - Full Text

Climatology, the study of climate, is no longer regarded as a single discipline that treats climate as something that fluctuates only within the unchanging boundaries described by historical statistics. The field has recognized that climate is something that changes continually under the influence of physical and biological forces and so, cannot be understood in isolation but rather, is one that includes diverse scientific disciplines that play their role in understanding a highly complex coupled “whole system” that is the Earth’s climate. The modern era of climatology is echoed in this book. On the one hand it offers a broad synoptic perspective but also considers the regional standpoint as it is this that affects what people need from climatology, albeit water resource managers or engineers etc. Aspects on the topic of climate change – what is often considered a contradiction in terms – is also addressed. It is all too evident these days that what recent work in climatology has revealed carries profound implications for economic and social policy; it is with these in mind that the final chapters consider acumens as to the application of what has been learned to date. This book is divided into four sections that cover sub-disciplines in climatology. The first section contains four chapters that pertain to synoptic climatology, i.e., the study of weather disturbances including hurricanes, monsoon depressions, synoptic waves, and severe thunderstorms; these weather systems directly impact humanity. The second section on regional climatology has four chapters that describe the climate features within physiographically defined areas. The third section is on climate change which involves both past (paleoclimate) and future climate: The first two chapters cover certain facets of paleoclimate while the third is centered towards the signals (observed or otherwise) of climate change. The fourth and final section broaches the sub-discipline that is often referred to as applied climatology; this represents the important goal of all studies in climatology–one that affects modes of living. Here, three chapters are devoted towards the application of climatological research that might have useful application for operational purposes in industrial, manufacturing, agricultural, technological and environmental affairs. Please click here to explore the components of this work.https://digitalcommons.usu.edu/modern_climatology/1014/thumbnail.jp

Estimation of global coastal sea level extremes using neural networks

Accurately predicting total sea-level including tides and storm surges is key to protecting and managing our coastal environment. However, dynamically forecasting sea level extremes is computationally expensive. Here a novel alternative based on ensembles of artificial neural networks independently trained at over 600 tide gauges around the world, is used to predict the total sea-level based on tidal harmonics and atmospheric conditions at each site. The results show globally-consistent high skill of the neural networks (NNs) to capture the sea variability at gauges around the globe. While the main atmosphere-driven dynamics can be captured with multivariate linear regressions, atmospheric-driven intensification, tide-surge and tide-tide non-linearities in complex coastal environments are only predicted with the NNs. In addition, the non-linear NN approach provides a simple and consistent framework to assess the uncertainty through a probabilistic forecast. These new and cheap methods are relatively easy to setup and could be a valuable tool combined with more expensive dynamical model in order to improve local resilience

Hydrometeorological Extremes and Its Local Impacts on Human-Environmental Systems

This Special Issue of Atmosphere focuses on hydrometeorological extremes and their local impacts on human–environment systems. Particularly, we accepted submissions on the topics of observational and model-based studies that could provide useful information for infrastructure design, decision making, and policy making to achieve our goals of enhancing the resilience of human–environment systems to climate change and increased variability

Selecting robust features for machine-learning applications using multidata causal discovery

Robust feature selection is vital for creating reliable and interpretable machine-learning (ML) models. When designing statistical prediction models in cases where domain knowledge is limited and underlying interactions are unknown, choosing the optimal set of features is often difficult. To mitigate this issue, we introduce a multidata (M) causal feature selection approach that simultaneously processes an ensemble of time series datasets and produces a single set of causal drivers. This approach uses the causal discovery algorithms PC1 or PCMCI that are implemented in the Tigramite Python package. These algorithms utilize conditional independence tests to infer parts of the causal graph. Our causal feature selection approach filters out causally spurious links before passing the remaining causal features as inputs to ML models (multiple linear regression and random forest) that predict the targets. We apply our framework to the statistical intensity prediction of Western Pacific tropical cyclones (TCs), for which it is often difficult to accurately choose drivers and their dimensionality reduction (time lags, vertical levels, and area-averaging). Using more stringent significance thresholds in the conditional independence tests helps eliminate spurious causal relationships, thus helping the ML model generalize better to unseen TC cases. M-PC1 with a reduced number of features outperforms M-PCMCI, noncausal ML, and other feature selection methods (lagged correlation and random), even slightly outperforming feature selection based on explainable artificial intelligence. The optimal causal drivers obtained from our causal feature selection help improve our understanding of underlying relationships and suggest new potential drivers of TC intensification

On the impacts of tropical cyclones in the Northeastern Atlantic

Tese de Mestrado, Ciências Geofísicas (Meteorologia e Oceanografia), 2023, Universidade de Lisboa, Faculdade de CiênciasTropical Cyclones (TCs) are one of the most deadly and destructive weather events. Their impacts are vast and often extend beyond wind damage or storm surges. Two examples of impacts are studied in this thesis, the first relating to the interaction of TCs with the ocean, and the second showing the impact of hurricane Ophelia in the exacerbation of the 2017 Portuguese October wildfires. This work shows the average upper-ocean response, in terms of chlorophyll-a (Chl a) and sea surface temperatures (SST), the passage of a TC in the Azores region between 1998 and 2020. Significant anomalies were found on the order of +0.050 mg m−3 (Chl a) and -1.615º C (SST). Furthermore, comparing these responses with TC characteristics revealed that the TC intensity was the most critical factor. Additionally, larger TCs and those occurring later in the season also produced stronger responses, while faster TCs only had this influence with Chl a. Two case studies were conducted for hurricanes Nadine (2012) and Ophelia (2017), revealing different impacts at different stages of the TCs for both variables, additionally the importance of TC track geometry is presented. The atmospheric conditions present during hurricane Ophelia (2017) over the Iberian Peninsula were explored using the WRF-ARW regional climate model. The simulations produced a quality ensemble which represented the observed situation. These conditions were analysed by looking at the Fire Weather Index (FWI) which presented over 80 % of the territory above the 99th percentile. Comparatively the enhanced FWI (FWIe), including an atmospheric instability component, presented over 90 % of the area above the same threshold for FWIe. The role played by Ophelia was shown to be crucial for the exacerbation of the fire risk through an increase in atmospheric instability and prevalent southern circulation.Esta dissertação apresenta dois estudos distintos sobre os impactos causados pelos ciclones tropicais (TCs) na região nordeste (NEA) do oceano Atlântico Norte (NA). Estes impactos são diversos e afetam diferentes sistemas (biológicos, sócio-económicos, etc.) com magnitudes distintas. Em primeiro lugar, foi analisado como a camada superficial do oceano reage à passagem dos TCs na região dos Açores, incluindo uma avaliação da resposta biofísica da superfície, baseada nas mudanças observadas na clorofila (Chl-a) e na temperatura da superfície da água do mar (SST). Em segundo lugar, é apresentada uma análise aprofundada ao aumento do perigo meteorológico de incêndio derivado à passagem off-shore do furacão Ophelia em outubro de 2017. Através de modelação numérica, foram analisadas as condições atmosféricas presentes desde o dia 14 até ao início de 17 de outubro de 2017, e comparadas com os dados de reanálise do ECMWF e também com os observados pelas estações do IPMA. Uma média (ensemble) de qualidade permite analisar as condições meteorológicas de incêndio e a respetiva influência da instabilidade atmosférica de forma exaustiva, concluindo-se que a influência do furacão Ophelia é significativa. Na região dos Açores, a existência de uma resposta biofísica após a passagem dum TC foi identificada a partir da análise de dados de Chl-a e SST, que produziram assinaturas anómalas positivas de Chl-a e negativas de SST. Esta assinatura é mais intensa para o caso da SST, na qual a passagem de um TC resulta numa anomalia induzida negativa em quase todos os pixels analisados (ou seja, arrefecimento). Em média, os TCs produziram anomalias positivas da ordem de 0,050 mg m−3 em relação à Chl-a e um arrefecimento médio da SST de 1,615º C. Os TCs mais intensos tendem a produzir respostas mais intensas, de acordo com a literatura sobre o tema. Constatou-se também que a velocidade de translação de um TC está associada às anomalias induzidas, embora a relação tenha sido considerada positiva e significativa (nível de confiança estatística de 95 %) apenas no caso da Chl-a. Constatou-se ainda uma ligação significativa com a área total impactada pelos TCs, contudo esta área pode aumentar devido a vários outros factores: TCs mais lentos impactam áreas maiores (devido à geometria da trajetória percorrida pelos mesmos); TCs mais intensos impactam áreas maiores; e TCs próximos da sofrerem uma transição pós tropical são geralmente maiores. Estes efeitos, quer individualmente quer combinados, podem afetar as anomalias induzidas de SST e Chl-a diferentes níveis. Além disso, verificou-se que a resposta oceânica foi maior durante os últimos meses do ano (parte final da época dos furacões no Atlântico), com uma relação significativa em ambas as variáveis. Isto deve-se à própria variabilidade sazonal, uma vez que os valores climatológicos normais para essa época do ano não são vistos durante condições excepcionais de TC (por exemplo a SST é normalmente mais fria no final do ano, mas as condições de TC exigem que seja mais elevada) e a resposta da superfície do oceano pode ajudar a área afectada a regressar a valores mais próximos da climatologia, em ambas as variáveis, em relação a essa época do ano. Adicionalmente dois casos de estudo foram avaliados mais ao pormenor, os furacões Nadine (2012) e Ophelia (2017). O furacão Nadine, um dos mais lentos TCs neste estudo, apresentou anomalias induzidas proeminentes, especialmente em relação à SST. Neste caso, considerando a baixa velocidade de translação do Nadine, o objetivo era estudar o impacto que as observações sobrepostas (pixels sobrepostos em sucessivos passos temporais como consequência do tempo permanecido sobre a mesma área) tinham nas anomalias induzidas. Esta análise demonstra que o impacto aumenta conforme o número de observações sobrepostas aumenta, implicando que a velocidade de translação lenta de Nadine e a geometria particular do seu percurso desempenharam um papel fundamental na resposta do oceano à passagem do TC. O furacão Ophelia representa um outro caso muito particular, uma vez que corresponde ao único grande furacão (acima de cat. 2) nesta região de estudo e quase toda a sua trajetória decorreu nesta área. O Ophelia originou fortes respostas anómalas tanto para a Chl-a como para a SST. Em relação à Chla, o Ophelia teve um impacto mais substancial no final da sua passagem pela região, revelando que a sua intensidade desempenhou um papel fundamental na indução de anomalias relacionadas com a Chla. Adicionalmente, a intensidade do stress do vento revela uma relação positiva e significativa com a evolução da tempestade e, portanto, a sua intensidade. Este facto traduz uma interação mecânica oceanoatmosfera crescente na fase mais madura da tempestade. Por outro lado, o Ophelia teve um impacto mais forte sobre a SST na sua ciclogénese em relação à fase mais tardia do ciclone, o que está provavelmente relacionado com as trocas termodinâmicas oceano-atmosfera durante a sua maturação. A simulação numérica da influência do furacão Ophelia nos incêndios de outubro de 2017 em Portugal foi estudada com o modelo de clima regional WRF-ARW v4.4.1. Foram realizadas doze simulações em dois domínios aninhados utilizando diferentes parametrizações para microfísica, convecção, e camada limite planetária. Foi produzido um conjunto médio final (ensemble) com as corridas que apresentaram a melhor semelhança estatística com os valores observados durante os três dias de simulações (14, 00 UTC - 17, 06 UTC de Outubro, 2017). Os campos obtidos com este ensemble foram comparados com os valores observados nas estações meteorológicas do IPMA, bem como com os dados do ECMWF, a reanálise ERA5. A comparação directa entre o ensemble e as observações do IPMA, utilizando a temperatura e o vento (velocidade e direcção), revelou inicialmente que as simulações representaram bastante bem o evento de calor extremo durante os dias 14 e 15 de outubro, bem como os ventos fortes observados durante o dia 15. Foi também observado que, no dia 16, as condições mudaram drasticamente para valores muito mais próximos daquela época do ano. Verificou-se ainda uma ligeira sobrestimação da temperatura durante a simulação mas, à medida que a simulação avançava, as temperaturas de superfície divergiam negativamente das observadas nas estações. Em relação ao vento, o ensemble mostrou uma boa semelhança com as observações, com uma ligeira sobrestimação sobre o continente. A análise ao dia mais afetado pelos incêndios e o seu precedente, 14 e 15 de outubro, revelou uma situação de extremo perigo meteorológico de incêndio, avaliado através do índice de perigo meteorológico de incêndio (FWI). No dia 15, a grande maioria de Portugal continental (82 %) apresentou valores acima do percentil 99 respetivo aos meses de verão. Estes valores correspondiam, em média, a valores FWI da ordem de 65, variando entre 50 e 80. As áreas mais impactadas (que eventualmente arderam) apresentaram valores com uma média próxima de 70. Estes valores de FWI representavam recordes históricos na grande maioria do país. Em ambos os dias, a instabilidade atmosférica sob a forma do CHI (Continuous Haines Index) também apresentava valores extremos acima do percentil 90; este valor diminuiu do primeiro para o segundo dia. O índice FWIe (Enhanced FWI) foi utilizado como meio para incorporar a instabilidade atmosférica (CHI) no FWI. A comparação directa entre o FWIe e o FWI mostra um aumento substancial relativo nos primeiros dias. O FWIe viu a área acima do percentil 99 aumentar em 11 pontos percentuais, para 93 %. Isto significou valores médios de FWIe na ordem dos 70, enquanto o lado superior da distribuição atingiu e ultrapassou os 80. Globalmente, as regiões com forte instabilidade atmosférica desempenharam um papel substancial no aumento dos valores, já recorde, do FWI. Finalmente, durante o dia 16 de outubro, o cenário mudou substancialmente. Durante este dia, as temperaturas diminuíram drasticamente e observou-se uma intrusão de ar frio e húmido a noroeste da Península Ibérica. Durante a tarde deste dia, e nas primeiras horas do dia seguinte, foram registadas quantidades substanciais de chuva que aliviaram o combate aos incêndios. No entanto, esperava-se que esta intrusão produzisse esta chuva mais cedo e o IPMA lançou avisos com esta previsão em mente. Isto levou com que algumas pessoas programassem propositadamente pequenas queimadas nas últimas horas do dia 15 de Outubro o que, dadas as circunstâncias, poderiam ter-se descontrolado. Este episódio de incêndio de Outubro de 2017 produziu mais de 200 000 ha de área queimada em cerca de 24h, sendo também responsável pela morte de 49 pessoas. Este evento composto (compound event) resultou numa mistura de condições extraordinárias iniciadas por uma mega-seca que vinha a assolar a Europa desde 2016, em conjunto com os ventos fortes para norte induzidos pelo furacão Ophelia. Esta configuração resultou no aumento das temperaturas, já elevadas, e ainda na diminuição da humidade relativa à superfície

Weather Extremes in a Warming Climate

Seit der industriellen Revolution haben Menschen durch Verbrennung von fossilen Energieträgern die Treibhausgaskonzentration in der Atmosphäre erhöht. Die daraus folgende Erderwärmung hat weitreichende Folgen für das Klima, unter anderem häufigere und intensivere Wetterextreme. Wegen ihrer gravierenden Auswirkungen auf die Gesellschaft, ist es von allgemeinem Interesse zu verstehen, wie der menschengemachte Klimawandel diese Wetterextreme beeinflusst. In dieser kumulativen Dissertation analysiere ich erst zwei komplexe Wettereignisse, die die Nahrungsmittelproduktion in Europa beeinträchtigen: Frosttage nach dem Beginn der Apfelblüte und Feuchte Frühsommerperioden nach warmen Wintern. In einer dritten Studie untersuche ich wie dynamische Klimaveränderungen in den mittleren Breiten der Nordhalbkugel zu beständigerem Sommerwetter beitragen. Schließlich beschäftige ich mich mit tropischen Stürmen im Nordatlantik und damit, wie sie von der globalen Erwärmung beeinflusst werden. Eine zentrale methodische Herausforderung in diesem Forschungsfeld ist, dass Wetterextreme per Definition selten sind und dass es aufgrund der starken internen Klimavariabilität schwierig ist, die Veränderungen zu quantifizieren, die auf den menschgemachten Klimawandel zurück zu führen sind. In dieser Arbeit verfolge ich zweigegenläufige Ansätze um mit dieser Herausforderung um zu gehen: 1) Ich verwende große Klimasimulationsensembles um den Effekt der internen Klimavariabilität aus zu glätten und dadurch die erzwungenen Veränderungen beim Apfelfrost und in der Persistenz zu ergründen. 2) Mit Methoden, die auf Beobachtungsdaten beruhen, quantifiziere ich den Einfluss der internen Klimavariabilität auf tropische Zyklone um dann einschätzen zu können, in welchem Maß der beobachtete Anstieg der tropischen Zyklonaktivität im Atlantik der internen Klimavariabilität oder erzwungenen Veränderungen zugeschrieben werden kann.Since the industrial revolution, humans have increased the greenhouse gas concentration of the atmosphere by burning fossil fuels. The resulting global warming has far reaching impacts on the climate system including increasingly frequent and intense weather extremes. Due to the severe impacts these weather extremes cause to societies, there is a strong interest in understanding how anthropogenic climate change affects weather extremes. In this cumulative thesis I first study two compound weather extremes that affect food production in Europe: frost days after apple blossom and wet early summers after warm winters. In a third study I quantify how dynamic changes in the climate system contribute to more persistent summer weather extremes in the northern hemispheric mid-latitudes. Finally, I analyze tropical cyclones in the Atlantic basin and changes in tropical cyclone activity as a result of global warming. One central methodological challenge in the research field is that weather extremes are rare by definition and that due to the strong internal climate variability it is difficult to quantify changes that are forced by anthropogenic climate change. In this thesis I explore two divergent approaches to this challenge: 1) Using large ensemble climate simulations I smooth out the effect of internal variability thereby exposing the forced change in apple frost and weather persistence. 2) Using observation based approaches, I quantify the contributions of internal climate variability on tropical cyclones in order to subsequently estimate to which extent the observed increase in tropical cyclone activity in the Atlantic can be attributed to internal climate variability or forced changes

Statistical/climatic models to predict and project extreme precipitation events dominated by large-scale atmospheric circulation over the central-eastern China

Global warming has posed non-negligible effects on regional extreme precipitation changes and increased the uncertainties when meteorologists predict such extremes. More importantly, floods, landslides, and waterlogging caused by extreme precipitation have had catastrophic societal impacts and led to steep economic damages across the world, in particular over central-eastern China (CEC), where heavy precipitation due to the Meiyu-front and typhoon activities often causes flood disaster. There is mounting evidence that the anomaly atmospheric circulation systems and water vapor transport have a dominant role in triggering and maintaining the processes of regional extreme precipitation. Both understanding and accurately predicting extreme precipitation events based on these anomalous signals are hot issues in the field of hydrological research. In this thesis, the self-organizing map (SOM) and event synchronization were used to cluster the large-scale atmospheric circulation reflected by geopotential height at 500 hPa and to quantify the level of synchronization between the identified circulation patterns with extreme precipitation over CEC. With the understanding of which patterns were associated with extreme precipitation events, and corresponding water vapor transport fields, a hybrid deep learning model of multilayer perceptron and convolutional neural networks (MLP-CNN) was proposed to achieve the binary predictions of extreme precipitation. The inputs to MLP-CNN were the anomalous fields of GP at 500 hPa and vertically integrated water vapor transport (IVT). Compared with the original MLP, CNN, and two other machine learning models (random forest and support vector machine), MLP-CNN showed the best performance. Additionally, since the coarse spatial resolution of global circulation models and its large biases in extremes precipitation estimations, a new precipitation downscaling framework that combination of ensemble-learning and nonhomogeneous hidden Markov model (Ensemble-NHMM) was developed, to improve the reliabilities of GCMs in historical simulations and future projection. The performances of downscaled precipitation from reanalysis and GCM datasets were validated against the gauge observations and also compared with the results of traditional NHMM. Finally, the Ensemble-NHMM downscaling model was applied to future scenario data of GCM. On the projections of change trends in precipitation over CEC in the early-, medium- and late- 21st centuries under different emission scenarios, the possible causes were discussed in term of both thermodynamic and dynamic factors. Main results are enumerated as follows. (1) The large-scale atmospheric circulation patterns and associated water vapor transport fields synchronized with extreme precipitation events over CEC were quantitatively identified, as well as the contribution of circulation pattern changes to extreme precipitation changes and their teleconnection with the interdecadal modes of the ocean. Firstly, based on the nonparametric Pettitt test, it was found that 23% of rain gauges had significant abrupt changes in the annual extreme precipitation from 1960 to 2015. The average change point in the annual extreme precipitation frequency and amount occurred near 1989. Complex network analysis showed that the rain gauges highly synchronized on extreme precipitation events can be clustered into four clusters based on modularity information. Secondly, the dominant circulation patterns over CEC were robustly identified based on the SOM. From the period 1960–1989 to 1990–2015, the categories of identified circulation patterns generally remain almost unchanged. Among these, the circulation patterns characterized by obvious positive anomalies of 500 hPa geopotential height over the Eastern Eurasia continent and negative values over the surrounding oceans are highly synchronized with extreme precipitation events. An obvious water vapor channel originating from the northern Indian Ocean driven by the southwesterly airflow was observed for the representative circulation patterns (synchronized with extreme precipitation). Finally, the circulation pattern changes produced an increase in extreme precipitation frequency from 1960–1989 to 1990–2015. Empirical mode decomposition of the annual frequency variation signals in the representative circulation pattern showed that the 2–4 yr oscillation in the annual frequency was closely related to the phase of El Niño and Southern Oscillation (ENSO); while the 20–25 yr and 42–50 yr periodic oscillations were responses to the Pacific Decadal Oscillation and the Atlantic Multidecadal Oscillation. (2) A regional extreme precipitation prediction model was constructed. Two deep learning models-MLP and CNN were linearly stacked and used two atmospheric variables associated with extreme precipitation, that is, geopotential height at 500 hPa and IVT. The hybrid model can learn both the local-scale information with MLP and large-scale circulation information with CNN. Validation results showed that the MLP-CNN model can predict extreme or non-extreme precipitation days with an overall accuracy of 86%. The MLP-CNN also showed excellent seasonal transferability with an 81% accuracy on the testing set from different seasons of the training set. MLP-CNN significantly outperformed over other machine learning models, including MLP, CNN, random forest, and support vector machine. Additionally, the MLP-CNN can be used to produce precursor signals by 1 to 2 days, though the accuracy drops quickly as the number of precursor days increases. (3) The GCM seriously underestimated extreme precipitation over CEC but showed convincing results for reproducing large-scale atmospheric circulation patterns. The accuracies of 10 GCMs in extreme precipitation and large-scale atmospheric circulation simulations were evaluated. First, five indices were selected to measure the characteristics of extreme precipitation and the performances of GCMs were compared to the gauge-based daily precipitation analysis dataset over the Chinese mainland. The results showed that except for FGOALS-g3, most GCMs can reproduce the spatial distribution characteristics of the average precipitation from 1960 to 2015. However, all GCMs failed to accurately estimate the extreme precipitation with large underestimation (relative bias exceeds 85%). In addition, using the circulation patterns identified by the fifth-generation reanalysis data (ERA5) as benchmarks, GCMs can reproduce most CP types for the periods 1960–1989 and 1990–2015. In terms of the spatial similarity of the identified CPs, MPI-ESM1-2-HR was superior. (4) To improve the reliabilities of precipitation simulations and future projections from GCMs, a new statistical downscaling framework was proposed. This framework comprises two models, ensemble learning and NHMM. First, the extreme gradient boosting (XGBoost) and random forest (RF) were selected as the basic- and meta- classifiers for constructing the ensemble learning model. Based on the top 50 principal components of GP at 500 hPa and IVT, this model was trained to predict the occurrence probabilities for the different levels of daily precipitation (no rain, very light, light, moderate, and heavy precipitation) aggregated by multi-sites. Confusion matrix results showed that the ensemble learning model had sufficient accuracy (>88%) in classifying no rain or rain days and (>83%) predicting moderate precipitation events. Subsequently, precipitation downscaling was done using the probability sequences of daily precipitation as large-scale predictors to NHMM. Statistical metrics showed that the Ensemble-NHMM downscaled results matched best to the gauge observations in precipitation variabilities and extreme precipitation simulations, compared with the result from the one that directly used circulation variables as predictors. Finally, the downscaling model also performed well in the historical simulations of MPI-ESM1-2-HR, which reproduced the change trends of annual precipitation and the means of total extreme precipitation index. (5) Three climate scenarios with different Shared Socioeconomic Pathways and Representative Concentration Pathways (SSPs) were selected to project the future precipitation change trends. The Ensemble-NHMM downscaling model was applied to the scenario data from MPI-ESM1-2-HR. Projection results showed that the CEC would receive more precipitation in the future by ~30% through the 2075–2100 period. Compared to the recent 26-year epoch (1990–2015), the frequency and magnitude of extreme precipitation would increase by 21.9–48.1% and 12.3–38.3% respectively under the worst emission scenario (SSP585). In particular, the south CEC region is projected to receive more extreme precipitation than the north. Investigations of thermodynamic and dynamic factors showed that climate warming would increase the probability of stronger water vapor convergence over CEC. More wet weather states due to the enhanced water vapor transport, as well as the increased favoring large-scale atmospheric circulation and the strengthen pressure gradient would be the factors for the increased precipitation