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\"Since 1980, the number of climate-related disasters has been greatly increased glocally. Scientific consensus based on the IPCC fifth report suggested that global warming would bring more intense and frequent extreme climate events. These climate-related disasters hinder the achievement of sustainable economic growth and prosperity by disrupting supply chains, impeding production, destroying infrastructure, and necessitating high-cost rebuilding and recovery. To mitigate the climate extreme risks and possible losses, it is essential to maximize the utilization of scientific outputs and to share best practices in disaster risk management. Aligned with such purposes, Asia-Pacific Economic Cooperation (APEC) Climate Center (APCC) hosts the APEC Climate Symposium (APCS) every year. APCS focused on drought predction and management in 2013, climate extremes and hydrological disaster in 2014, and efficient use of climate information for disaster risk management in 2015. This book aims to compile some of the important results from the latest research in climate extreme prediction and services and its application studies with a focus on climate extremes such as typhoons, droughts, and floods based on the APCS presentations during 2013-2015\"-- Page 4 of cover.

In this work, the trend of extreme rainfall indices in the macrometropolis of São Paulo (MMSP) was analyzed and correlated with large-scale climatic oscillations. A cluster analysis divided a set of rain gauge stations into three homogeneous regions within MMSP, according to the annual cycle of rainfall. The entire MMSP presented an increase in the total annual rainfall, from 1940 to 2016, of 3 mm yr−1 on average, according to a Mann–Kendall test. However, there is evidence that the more urbanized areas have a greater increase in the frequency and magnitude of extreme events while coastal and mountainous areas, and regions outside large urban areas, have increasing rainfall in a better-distributed way throughout the year. The evolution of extreme rainfall (95th percentile) is significantly correlated with climatic indices. In the center-north part of the MMSP, the combination of Pacific decadal oscillation (PDO) and Antarctic Oscillation (AAO) explains 45% of the P95th increase during the wet season. In turn, in southern MMSP, the temperature of South Atlantic (TSA), the AAO, El Niño–South Oscillation (ENSO), and the multidecadal oscillation of the North Atlantic (AMO) better explain the increase in extreme rainfall (R² = 0.47). However, the same is not observed during the dry season, in which the P95th variation was only negatively correlated with the AMO, undergoing a decrease from the 1970s until the beginning of this century. The occurrence of rainy anomalous months proved to be more frequent and associated with climatic indices than was the occurrence of dry months.

Arid and semi-arid regions are vulnerable to natural disturbances and particularly, to anthropogenic climate change. In these regions, water resources are scarce, and groundwater is commonly the main source of water for municipal supply, agricultural, and industrial purposes. Groundwater overexploitation is a common issue for these regions, where overextraction, climate variability, and recharge are responsible for groundwater storage evolution. This study aims to find teleconnections through wavelet analysis between inter-annual rates of change in storage, obtained from geostatistical groundwater modeling, and climate variability. Continuous wavelet transform, cross-wavelet analysis, and wavelet coherence were used to analyze and characterize non-stationary patterns of variability of precipitation (PP), standardized precipitation index (SPI), minimum (Min_Temp) and maximum temperature (Max_Temp), Enhanced Vegetation Index (EVI), the Oceanic Niño Index (ONI), the Pacific-North American pattern (PNA), the Caribbean Index (CAR), and groundwater change in storage (ΔGS) semestral time series. The results suggest significant influences of the indices on ΔGS, PP, Min_Temp, and EVI. PNA manifested an important influence in short periods (2–4 years) in all aquifers, ONI showed influence mainly over long periods (8–16 years), and CAR had influence in periods of high hurricane activities. In addition, under extreme climatic conditions (El Niño or La Niña) the higher/lower water demand increase their influence on groundwater level response creating an indirect link between climate indices and ΔGS. This research can serve as a good indicator of the climate variations behavior and can provide information to understand the regional climate effects of the ENSO phenomenon in overexploited aquifers. Graphical abstract

Frequent occurrences of extreme heats in recent years pose a threat to the local society, economy, and ecosystem in Southern China. Understanding the process and predictability of these heat events is crucial for scientific research and policy-making. The long-term variation characteristics and the corresponding driving factors of high-temperature days (HTDs) and surface air temperature (SAT) in midsummer in Southern China were analyzed by observed data of meteorological stations and atmospheric reanalysis data. The results reveal an abrupt rise in HTDs around the early 2000s during the period of 1979–2021, rather than a sustained increase. Prior to and after this mutation, no clear trend is observed. The changes in SAT align closely with the variations in HTDs. The abrupt increase in solar radiation, resulting from a decrease in total cloud cover, is identified as the direct cause of the SAT change. In the decade following the abrupt change point, significant anomalous descent motion and low-level divergent winds over Southern China are observed, creating favorable climatic conditions for the rise in SAT. The primary driving factor behind these circulation changes is identified as the Pacific interdecadal oscillation, which shifted from a positive to a negative phase during the same period. This shift led to the strengthening of the Walker circulation and the formation of an anomalous anticyclone over the northwest Pacific. It is worth noting that current mainstream climate dynamical models failed to capture the abrupt rise in SAT, instead exhibiting a sustained increasing trend that compromised the accuracy of the forecast. This discrepancy may be attributed to the models' slow response to rapid changes in sea surface temperature.

Brazil is increasingly affected by extreme weather events due to climate change, with pronounced regional differences in temperature and precipitation patterns. The southeast region is particularly vulnerable, frequently experiencing severe droughts and extreme heatwaves linked to atmospheric blocking events and intense rainfall episodes driven by the South Atlantic Convergence Zone (SACZ). These phenomena contribute to recurring climate-related disasters. The country’s heavy reliance on hydropower heightens its susceptibility to droughts, while growing evidence points to intensifying dry spells and wildfires across multiple regions, threatening agricultural output and food security. Urban areas, particularly, are experiencing more frequent and severe heatwaves, posing serious health risks to vulnerable populations. This study investigates the links between global teleconnection indices and synoptic-scale systems, specifically blocking events and SACZ activity, and their influence on Brazil’s extreme heat, drought conditions, and river flow variability over the past 30 to 40 years. By clarifying these interactions, the research aims to enhance understanding of how large-scale atmospheric dynamics shape climate extremes and to assess their broader implications for water resource management, energy production, and regional climate variability.

The waters of the Paraíba do Sul River supply around 15 million people, most of whom live in metropolitan regions of the state of Rio de Janeiro. Climate change alters its precipitation regime and can cause an increase in the occurrence of extreme hydrological events. Furthermore, the variability of precipitation can result from the combined effects of the surface conditions of the oceans and the variations in the dynamics of atmospheric systems. This work aims to detect possible changes in the climatic extremes of precipitation in the Paraíba do Sul hydrographic basin and to investigate evidences of correlation of these indices with the oceanic oscillations associated with the El Niño-Southern Oscillation (ENSO), Pacific Decadal Oscillation (PDO) and Atlantic Multidecadal Oscillation (AMO). Results indicate that the northeast sector of the basin present trends of increase in the total annual precipitation, in the number of very humid days and in the occurrence of extreme events, in a space of time up to five days. The southwest sector, on the other hand, show decreasing trends in total annual precipitation, in the number of very humid days, but with an increase trend in the maximum amount of rainfall on five consecutive days. The central sector has characteristics of a transition zone. The correlation analyzes show that oceanic oscillation indices have a non-significant correlation with most annual extreme precipitation indices, except for La Niña (El Niño), that can increase (decrease) the number of consecutive dry days in the region. Besides, for austral autumn, La Niña (El Niño) can decrease (increase) the precipitation in the basin, and in austral winter, hot (cold) phase of AMO can decrease (increase) the precipitation. For austral spring, two teleconnections were found: hot (cold) phase of PDO can increase (decrease) the precipitation in the southwest region of the basin, and during El Niño (La Niña) years, negative (positive) precipitation anomalies tend to occur in the northeast sector of the basin, while positive (negative) anomalies appear in its southwest region. The wavelet spectrum of precipitation anomaly indicates significant values with low power that could be correlated with ENSO, corroborating the results of seasonal correlations.

The lack of long-term meteorological observations largely hinders a comprehensive assessment of the changes in extreme precipitation in the high-mountainous region of the Yarlung Zanbo (YZ) river basin. In this study, the YZ basin was used as an example to clarify extreme changes based on a newly generated precipitation dataset in Tibetan Plateau (TP) basins with complex terrain and limited observations. Results showed that divergent changes in extreme precipitation were observed in the YZ basin. Extreme precipitation indices showed inconsistent changes in the YZ basin for this period. The duration-based consecutive wet day (CWD) indices (5 days/10 year) showed significant increasing trends, while the other eight indices showed insignificant change. It generally mutated in 1989, which showed increased trends for 1961–1988, but decreased trends for 1989–2020. Mean annual extreme precipitation increased from the upper stream to the downstream sub-basin of the YZ basin, and generally decreased from low altitudes to high altitudes. There were opposite tendencies of extreme precipitation between 1961 and 1988 and 1989–2020 across sub-basins. Extreme precipitation showed decreasing trends for the upper sub-basins of the Lhatse (LZ), Rikaze (RKZ), Lhasa (LS) and region between LZ and Nuxia hydrological station (LZ-NX), with an increasing trend in the region between NX and Pasighat (NX-BXK) for 1961–1988, with the opposite occurring during 1989–2020. The annual change in extreme precipitation was mostly influenced by the Arctic Oscillation (AO), East Asian Summer Monsoon (EASM), Pacific Decadal Oscillation (PDO) and Atlantic Multidecadal Oscillation (AMO) in the YZ basin for 1961–2020. However, the influence of large-scale climatic oscillation on extreme precipitation varied at different sub-basins and periods. We expect these findings will improve our understanding of the extreme precipitation characteristics in the TP basins.

Under the background of global warming, the frequency and intensity of extreme climate have increased, especially extreme high temperatures. In order to correctly predict the changes in the extreme high temperatures in summer in China in this century, it is urgent to deepen the understanding of the characteristics and physical mechanisms of the extreme high temperatures in summer on the centennial timescale. Many researchers have explored the mechanism of the influences of the variability of the solar cycle on climate change, while the mechanism of the influences of the centennial variation of solar activity on climate change remains elusive. Here, we use the outputs from the Control (CTRL) experiment, Total solar irradiance and Orbital (TSI_ORB) experiment, and Orbital (ORB) experiment from Nanjing Normal University-Holocene (NNU-Hol) experiments to study the extreme high temperatures in summer in China during the Holocene. On the basis of verifying the consistency of the centennial period between the TSI (TSI_ORB minus ORB plus CTRL) experiment and the reconstructed data, we compared the centennial variation characteristics of the summer extreme high temperature in the CTRL experiment and the TSI experiment. It shows that under the modulation of total solar irradiance, the centennial spatial pattern of the summer extreme high temperatures changed from dipole mode to uniform mode, with 300-year and 500-year periodicity, compared to the influence of only internal variability. On the centennial time scale, the greatest difference is located in northeast China. The subsidence movement and the reduction of cloud cover caused by the anticyclone under the control of high-pressure lead to the increase of downward solar radiation, thus making a positive center is showed in northeast China on the impacts of total solar irradiance. Furthermore, the center of the Rossby wave train in the barotropic structure of the upper circulation related to the summer extreme high temperature significantly moves northward. This barotropic structure is composed of continuous pressure ridges from Eurasia to North America and the North Atlantic, which is conducive to the increase of the summer extreme high temperatures. Furthermore, we investigated the underlying physical mechanisms. Under the influence of total solar irradiance, the Pacific Decadal Oscillation (PDO) with the same centennial cycle as extreme high temperatures lead to obvious subsidence movement and increase of radiation flux, causing an increase in extreme high temperatures over northeast China.

The nonstationarity of extreme precipitation is now well established in the presence of climate change and low-frequency variability. Consequently, the implications for urban flooding, for which there are not long flooding records, need to be understood better. The vulnerability is especially high in coastal cities, where the flat terrain and impervious cover present an additional challenge. In this paper, we estimate the time-varying probability distributions for hourly and daily extreme precipitation using the Generalized Additive Model for Location Scale and Shape (GAMLSS), employing different climate indices, such as Atlantic Multi-Decadal Oscillation (AMO), the El Niño 3.4 SST Index (ENSO), Pacific Decadal Oscillation (PDO), the Western Hemisphere Warm Pool (WHWP) and other covariates. Applications to selected coastal cities in the USA are considered. Overall, the AMO, PDO and WHWP are the dominant factors influencing the extreme rainfall. The nonstationary model outperforms the stationary model in 92% of cases during the fitting period. However, in terms of its predictive performance over the next 5 years, the ST model achieves a higher log-likelihood in 86% of cases. The implications for the time-varying design rainfall in coastal areas are considered, whether this corresponds to a structural design or the duration of a contract for a financial instrument for risk securitization. The opportunity to use these time-varying probabilistic models for adaptive flood risk management in a coastal city context is discussed.

In this study, we propose a regional Bayesian hierarchical model for flood frequency analysis. The Bayesian method is an alternative to the traditional regional flood frequency analysis. Instead of relying on the delineation of implicit homogeneous regions, the Bayesian hierarchical method describes the spatial dependence in its inner structure. Similar to the classical Bayesian hierarchical model, the process layer of our model presents the spatial variability of the parameters by considering different covariates (e.g., drainage area, elevation, precipitation). Beyond the three classical layers (data, process, and prior) of the Bayesian hierarchical model, we add a new layer referred to as the “L-moments layer”. The L-moments layer uses L-moments theory to select the best-fit probability distribution based on the available data. This new layer can overcome the subjective selection of the distribution based on extreme value theory and determine the distribution from the data instead. By adding this layer, we can combine the merits of regional flood frequency and Bayesian methods. A standard process of covariates selection is also proposed in the Bayesian hierarchical model. The performance of the Bayesian model is assessed by a case study over the Willamette River Basin in the Pacific Northwest, U.S. The uncertainty of different flood percentiles can be quantified from the posterior distributions using the Markov Chain Monte Carlo method. Temporal changes for the 100-year flood percentiles are also examined using a 20- and 30-year moving window method. The calculated shifts in flood risk can aid future water resources planning and management.