Two or more spatio-temporally co-located meteorological/climatological extremes (co-occurring extremes) place far greater stress on human and ecological systems than any single extreme could. This was observed during the California drought of 2011–2015 where multiple years of negative precipitation anomalies occurred simultaneously with positive temperature anomalies resulting in California’s worst drought on observational record. The large-scale drivers which modulate the occurrence of extremes in two or more variables remains largely unexplored. Using California wintertime (November–April) temperature and precipitation as a case study, we apply a novel, nonparametric conditional probability distribution method that allows for evaluation of complex, multivariate, and nonlinear relationships that exist among temperature, precipitation, and various indicators of large-scale climate variability and change. We find that multivariate variability and statistics of temperature and precipitation exhibit strong spatial variation across scales that are often treated as being homogeneous. Further, we demonstrate that the multivariate statistics of temperature and precipitation are highly non-stationary and therefore require more robust and sophisticated statistical techniques for accurate characterization. Of all the indicators of the large-scale climate conditions we studied, the dipole index explains the greatest fraction of multivariate variability in the co-occurrence of California wintertime extremes in temperature and precipitation.

Wildfires are increasingly threatening Nepal, particularly during the dry pre-monsoon months (March-May), leading to severe ecological impacts and disruptions to local communities. To improve wildfire prediction and preparedness, this study evaluated four advanced machine learning algorithms-Random Forest, Radial Basis Function Neural Network, Artificial Neural Network, and Support Vector Machine-using comprehensive dataset (2001-2023) of meteorological, topographical, anthropogenic, locational, and vegetation variables. The Random Forest (RF) model outperformed others, achieving the highest accuracy (88.6%) and predictive reliability (AUC: 0.96). Notably, vapor pressure deficit emerged as the strongest predictor, contrasting previous studies where precipitation was often considered dominant. Utilizing the robust RF model, a high resolution (1-km) wildfire risk map identified 11.1% of Nepal, encompassing 12 districts and 48 municipalities primarily in the southwestern region, as very high-risk areas. By integrating daily meteorological data into wildfire predictions, this research provides an innovative framework that enhances risk management strategies, offering actionable insights for decision-makers and supporting resilience-building efforts in fire prone regions.

The Arctic has warmed significantly faster than the rest of the globe, leading to rapid sea ice decline. Anthropogenic aerosols are traditionally viewed as cooling agents that do not contribute to Arctic sea ice loss. Here we investigate how aerosol-induced changes in atmospheric circulation patterns contribute to Arctic sea ice decline using a fully-coupled global earth system model. We compared single forcing experiments to examine individual and combined effects of greenhouse gases and anthropogenic aerosols. Aerosols contribute to intensification of the North Pacific anticyclone, which enhances heat transport into the Arctic through the Bering Strait. When combined with greenhouse gas-induced warming, aerosols have a greater impact on Arctic sea ice decline in the western Chukchi Sea compared to when either forcing acts independently. This compound effect challenges the traditional view of aerosols as solely cooling agents, demonstrating that anthropogenic aerosols can accelerate Arctic sea ice melting.

The previous establishment of Taiwan's Forest Fire Danger Rating Warning System in 2002 relied solely on ground-based fuel aridity considerations, leaving a gap between fire weather information and fire detection. In view of the warming climate and the increasing trend in wildfire frequency, this study analyzes meteorological conditions that promote wildfire occurrence using modern atmospheric reanalysis and satellite data. Correlation coefficient matrix and generalized additive models (GAMs) were constructed to investigate the associations between the interannual variance of wildfire occurrence during regional fire seasons over a 58-yr period (1964-2021) and various fire-related indices, including meteorological conditions, vegetation, soil moisture, and collective indicators represented by the fire weather index (FWI). The study finds that precipitation and humidity, rather than temperature, are the primary climatic drivers of increased wildfire risk in Taiwan. Specifically, prolonged droughts during the summer and fall enhance fuel aridity, deplete groundwater, and deteriorate vegetation, subsequently increasing wildfire probability. Furthermore, a significant inverse relationship between southwestern wildfires and antecedent groundwater 12 months prior suggests that fuel fammability and availability preceding drought events compound the likelihood of widespread wildfires. This study highlights a robust tendency toward increased wildfires in response to drought starting almost a year before, suggesting the feasibility of seasonal prediction application.

This study leverages the Global/Regional Integrated Model system (GRIMs) version 4.0 climate model to examine the mechanisms behind the recent intensification of winter stationary waves over western North America. Prescribed sea surface temperature warming forces a strengthening of westerly winds, amplifying the ridge that characterizes the stationary waves in western North America. The streamfunction budget analysis reveals relative vorticity advection is mainly associated with this process. We further show that ocean warming is the primary driver of changes in westerly winds and stationary waves in the Northern Hemisphere. Sea ice losses exert a considerable effect through a different mechanism, complementing the dominant influence of ocean warming on these atmospheric changes. Our results thus reveal the crucial role tropical oceans play in modulating global warming’s effect on the stationary waves in the Northern Hemisphere and add a more quantitative perspective to the previously reported influence of Arctic amplification.

The western United States (U.S.) has been experiencing more severe wildfires, in part due to climate change, but the underlying synoptic patterns and their modulation in driving fire weather is unclear. Here we investigated the relationship between weather regimes (WRs) and fire weather indices, specifically vapor pressure deficit (VPD) and the Canadian Forest Fire Weather Index. By identifying five singular WRs using k-means clustering, we found that a particular regime (WR-2), one characterized by a distinct tripolar wave train pattern over the continental U.S., has exhibited an increased frequency since 1980. The ascribed WR-2 regime was found to be mainly responsible for rising trends in the fire weather indices, especially VPD. Further, the average fire indices of the WR-2 regime played a more important role than the frequency in shaping the rising trends in the fire weather indices. The increased frequency of the WR-2 WR was mainly attributed to anthropogenic forcing and, the year-to-year variation of the frequency was associated with sea surface temperature anomalies over the subtropical eastern Pacific. Human-induced climate change might have furthered the exacerbation of wildfire danger in the western U.S. by modulating the behaviors of WRs and fire weather indices.

Alaska is experiencing simultaneous trends of increased winter wetness and heightened summer fire risk due to global warming, leading to more frequent wildfires and greater unpredictability in fire behavior in recent decades. Large-ensemble simulations show that warming drives distinct seasonal changes: in winter, an intensified ridge over the western U.S. enhances moisture transport to Alaska, increasing precipitation while promoting vegetation growth near the Alaska Range. In summer, rising temperatures intensify the fire weather index signaling greater wildfire potential and increase lightning activity. Although the links among these complex seasonal changes remain difficult to validate, temporal overlap—enhanced vegetation growth followed by more fire-conducive weather, and associated increase in lightning could collectively heighten wildfire risk. The robustness of our large-ensemble simulations provides compelling evidence for these cascading effects. Extreme lightning-driven events, such as the Swan Lake Fire, represent the emerging pattern in Alaska’s evolving fire regime. The concurrent rise in winter wetness and summer fire conditions underscore the urgent need for adaptive fire management strategies that address these interconnected climate drivers.

This study investigates the contrasting trends in extreme Tmax events and extreme wet-bulb temperature (Tw) events across the monsoon and arid regions of Asia using the ERA5 reanalysis dataset. Our analysis reveals a substantial shift in the monsoon region, where extreme Tw events have risen by 1.95 days, outpacing the increase in extreme Tmax events. In the arid region, extreme Tmax events have increased more significantly, exceeding extreme Tw by an average of 2.05 days in recent years, reflecting the limited moisture availability in this area. Spatiotemporal analyses also reveal the widespread prevalence of humid-heat extremes in monsoon Asia and the intensification of primarily dry heat extremes in arid regions. These divergent trajectories highlight the pivotal role of climatological differences, with the change in monsoonal circulations amplifying humid extremes, while the inherent aridity constrains humidity increases. Our findings emphasize the need for regional adaptation strategies and mitigation efforts to address the escalating impacts on human society and ecosystems across Asia's climatic divide.

While decadal predictability is one of the key information demands on marine management and conservation endeavors, the lack of long-term observed records and complex climate variability challenges understanding it. Seagrass beds are not only important blue carbon sinks but also crucial habitats and feeding grounds for diverse marine organisms. This study uses in-situ data from 2001 to 2021 to investigate the primary decadal environmental control, the Pacific Meridional Mode, on seagrass growth in southern Taiwan. Two primary seagrass metrics, aboveground biomass and cover, were examined against various environmental and meteorological variables. Our initial findings reveal a significant correlation between PMM and seagrass growth. Aboveground biomass exhibits a robust negative correlation with PMM, while cover displays a weaker yet positive association. Further examination of regional climate dynamics unveils notable shifts in surface solar radiation, temperature, and rainfall concerning seagrass. Specifically, increased aboveground biomass coincides with reduced solar radiation, lower temperatures, and enhanced rainfall in southern Taiwan, resembling a negative PMM-like pattern. This pattern underscores the sensitivity of aboveground biomass to large-scale climatic fluctuations across the Pacific basin. Conversely, seagrass cover demonstrates opposing patterns compared to aboveground biomass but with less statistical significance. This suggests that cover growth is influenced by a broader array of factors, resulting in a more nonlinear response. In essence, our research underscores the vital role of PMM and regional climate conditions in shaping tropical seagrass growth, offering further insights for marine conservation efforts.

The Indo-Gangetic Plain has experienced a substantial rise in relative humidity in recent decades, with implications for human health and well-being. Here we use atmospheric reanalysis and large-ensemble climate model simulations to assess changes since the 1960s. Relative humidity increased by 10.3 +/- 0.3 percent, mainly due to a 2.9 +/- 0.1 grams per kilogram rise in specific humidity and a slight decrease in air temperature (-0.2 +/- 0.1 degrees Celsius). Aerosol-induced surface cooling played a crucial role in enabling this moistening. Decomposition analysis reveals that specific humidity accounts for 95% of the increase, with cooling explaining the rest. Future projections show contrasting trends. High-emission scenarios peak and then decline after the 2040s, as greenhouse gas warming overtakes weakening aerosol effects. In contrast, low-emission scenarios maintain stable or slightly increasing humidity. These findings reveal how aerosols and greenhouse gases exert opposing influences on humidity and underscore the need for coordinated climate strategies in this vulnerable region.

Evaluating forecast models encompasses assessing their ability to accurately depict observed climate states and predict future climate variables. Various evaluation methods, from computationally efficient measures like the anomaly correlation coefficient to more intricate approaches, have been formulated. While simpler methods provide limited information, climatology, due to its simplicity and immediate linkage to model performance, is a commonly utilized primary evaluation metric. In this study focusing on temperature and precipitation, we propose a novel metric based on the model’s mean state, integrating both climatology and the seasonal cycle for a more accurate assessment of the relationship between mean state performance and prediction skill on weather and sub-seasonal time scales compared to relying solely on climatology. This integrated metric reveals a robust correlation between temperature and precipitation across diverse geographical locations, with a more pronounced effect in tropical areas when considering the seasonal cycle. Additionally, we find that temperature exhibits higher prediction skill compared to precipitation. The discovered relationship serves as a potential early indicator for predicting the efficacy of Seasonal to Sub-seasonal (S2S) models and offers valuable insights for model development, emphasizing the significance of this integrated metric in enhancing S2S model performance and advancing climate prediction capabilities.

This study conducts a comprehensive analysis of the influence of tropical cyclones on precipitation variations in Indochina, examining Vietnam, Laos, and Cambodia, while exploring their connection with evolving climatic variables. Covering a span of four decades (1979–2021) and integrating daily precipitation records with climatic datasets, the research elucidates tropical cyclone’s contributions to the annual precipitation across distinct regions, revealing percentages of 27

The 2020-2021 record drought in Taiwan halted carbon sequestration in its predominantly evergreen subtropical forests. The analysis uncovers a significant correlation between net ecosystem exchange, radiative factors, groundwater levels, and wildfires, indicating that the severity of droughts leads to a shift from carbon absorption to emission in these forests, thereby inviting a broader examination of the climate-carbon nexus in future scenarios.

Probable maximum precipitation (PMP) has been an important criterion for designing hydrological infrastructure and it is likely to change with respect to global warming. To assess the potential risk that hydrological infrastructure in the U.S. state of Utah may encounter under the least mitigated emission scenario of Representative Concentration Pathway 8.5 (RCP8.5), this study identified historical (1950–2005) and future (2006–2100) PMP estimates to quantify the range and degree of change for extreme precipitation. The results show that an averaged RCP8.5 simulated 19.2% (3.43°C) increase in dewpoint temperature will result in a 39% (88.39 mm) increase in 24‐h 100‐mi2 (259 km2) PMP values. It is also found that the rapidly growing metropolitan areas of the state would experience a greater PMP increase (106.43 mm) than that in the state's National Parks and forested areas (93.98 mm). This discovery indicates a vulnerability that could affect both hydrological and metropolitan infrastructure. The planning of the state's infrastructure needs to consider the changing PMP under global warming.

Unexpected and large spring precipitation events in the Colorado River Basin (CRB) that significantly alleviated an otherwise severe water shortage have been observed for over a century, such as the "Miracle May" of 2015. Although these events are often termed as "drought-busting" or "miracle events" by water managers and the media, they have not been extensively researched or characterized. In this collaborative study with water managers across the CRB, we propose a definition for these hard-to-predict, ultra-high precipitation events occurring during the late-snow or snowmelt season. This characterization provides a framework for quantifying the frequency and intensity of extreme dry-to-wet springtime transitions. Despite limitations of climate model simulations due to uncertainties and the inhomogeneous qualities, our findings suggest that such transitions may become less frequent and less intense in a warming climate. In view of the potentially wetter but less-snowy climate in the basin, the need for future research to more quantitatively assess these "miracle events" is emphasized.

In this short communication, we report initial success in utilizing existing Explainable Artificial Intelligence (XAI) methodology to investigate an emerging precursor of the El Ni & ntilde;o-Southern Oscillation (ENSO), manifest as sea surface temperature anomalies (SSTA) in the Western North Pacific (WNP), and its impact on enhancing ENSO prediction accuracy. Our analysis reveals that integrating WNP SSTA with established XAI techniques significantly increases the predictability of ENSO states. We found marked improvement in prediction accuracy, from a 60 % baseline to over 85 % for forecasting moderate warm, cold, and neutral ENSO states one year ahead. For higher magnitude events, precision surpasses 90 %. This work, intended as a follow-up to recent studies, underscores the potential of augmenting emerging XAI with additional SST signals to advance long-term climate forecasting capabilities.

Wildfires are a significant environmental hazard that pose threats to ecosystems, human livelihoods, and infrastructure. The impact of climate change on wildfires has been widely documented, and Taiwan, an island in East Asia, is no exception to this phenomenon. Given the increasing frequency and intensity of drought conditions in recent years, there is a pressing need to better understand and predict future wildfire risk in Taiwan. In this study, we evaluate changes in wildfire potential during historical and future periods based on satellite observation and regional downscaled projection data. Additionally, we investigate the relationship between past climate conditions in Taiwan and the occurrence of wildfires to gain insights into the characteristics of wildfires and estimate future wildfire frequency under the influence of climate change. Our findings reveal a significant upward trend in historical temperature and wind speed in Taiwan, accompanied by increased variability in rainfall and humidity, and the alternation of which has resulted in a significant increase in wildfire risk. Notably, wildfires in Taiwan are found to be more influenced by the degree of dryness rather than extreme high temperatures. When compared to the baseline of the average wildfire occurrences in recent years (1992–2021), the projected increase in the late twenty-first century (2070–2099) is approximately 35.6

The regional climate variability in peninsular Southeast Asia (PSEA) can influence springtime biomass burning (BB) aerosol emissions and associated transport patterns. To comprehend the interannual variation of regional climate and its impact on PSEA BB, a diagnostic analysis based on the Modern-Era Retrospective Analysis for Research and Applications version 2 (MERRA-2) dataset and Moderate Resolution Imaging Spectroradiometer (MODIS) observations from 2000 to 2019 has presented. Employing principal component, composite, and correlation analyses, this study identified four climatic factors governing the emission and transport of PSEA BB aerosols: (i) a low-level anticyclone (suppressed monsoon trough) in the Bay of Bengal, (ii) the relative strength of the anticyclone over the South China Sea, (iii) the Pacific subtropical high, and (iv) low-level westerlies from PSEA to Taiwan. Additionally, BB emissions and transport significantly correlate with the El Niño-Southern Oscillation (ENSO). In the El Niño year, increased anticyclones in the Bay of Bengal and South China Sea accompanied the stronger westerlies, which enhanced BB aerosol emission and transport. The diagnostic results of this study can contribute to a better understanding and improved model simulations of aerosol-climate interactions in South and Southeast Asian monsoon regions.

We present a comprehensive analysis diagnosing the primary factors driving the observed changes in major atmospheric teleconnection patterns in the Northern Hemisphere winter, including the Pacific North American pattern (PNA), North Atlantic Oscillation (NAO), and North American winter dipole (NAWD), with particular focus on their roles in shaping anomalous weather across North America. Our investigation reveals a consistent influence of the NAWD over seven decades, contrasting with fluctuating impacts from PNA and minor impacts from NAO. In particular, an emergent negative correlation between the NAWD and PNA, signaling a shifted phase of teleconnection patterns, is identified. Such a relationship change is traced to enhanced upper-level ridges across western North America, reflecting a reinforced winter stationary wave. Through attribution analysis, we identify greenhouse gas emissions as a probable driver for the northward drift of the Asia-Pacific jet core, which, aided by orographic lifting over the Alaskan Range, subsequently amplifies the winter stationary wave across western North America. This work emphasizes the pronounced effect of human-induced global warming on the structure and teleconnection of large-scale atmospheric circulation in the Northern Hemisphere winter, providing vital perspectives on the dynamics of current climate trends.

Summer precipitation in the Three Rivers Source Region (TRSR) of China is vital for the headwaters of the Yellow, Yangtze, and Lancang rivers and exhibits significant interdecadal variability. This study investigates the influence of the East Asian westerly jet (EAWJ) on TRSR rainfall. A strong correlation is found between TRSR summer precipitation and the Jet Zonal Position Index (JZPI) of the EAWJ from 1961 to 2019 ( R = 0.619, p < 0.01). During periods when a positive JZPI indicates a westward shift in the EAWJ, enhanced water vapor anomalies, warmer air, and low-level convergence anomalies contribute to increased TRSR summer precipitation. Using empirical orthogonal function and regression analyses, this research identifies the influence of large-scale circulation anomalies associated with the Atlantic-Eurasian teleconnection (AEA) from the North Atlantic (NA). The interdecadal variability between the NA and central tropical Pacific (CTP) significantly affects TRSR precipitation. This influence is mediated through the AEA via a Rossby wave train extending eastward along the EAWJ, and another south of 45 degrees N. Moreover, the NA-CTP Opposite Phase Index (OPI), which quantifies the difference between the summer mean sea surface temperatures of the NA and the CTP, is identified as a critical factor in modulating the strength of this teleconnection and influencing the zonal position of the EAWJ.