ABSTRACT Rising temperatures and rapid urbanization have intensified heat stress risks in Ghana's coastal cities. However, limited knowledge exists on long‐term human thermal comfort variability and its relationships with large‐scale climate indices in tropical coastal settings. This study investigated four‐decade patterns of outdoor thermal comfort along Ghana's coastal‐urban corridor from 1980 to 2020 using the Universal Thermal Climate Index (UTCI). Maximum UTCI increased significantly by 0.029°C year −1 , very strong heat‐stress days increased by 1.393 days year −1 , and comfortable conditions declined across most regions. Inland cities experienced stronger warming, whereas coastal centres exhibited more moderate increases, a pattern that may be consistent with coastal climatic moderation. Change point analysis identified significant shifts in 1998 and 2002, broadly coinciding with El Niño events and accelerated 21st‐century warming. Random Forest modelling with temporal validation (training: 1980–1999; testing: 2000–2020) explained a modest 22% of out‐of‐sample monthly UTCI variance ( R 2 = 0.22; RMSE = 1.47°C). The analysis emphasised relative predictor importance rather than predictive accuracy. SHAP results identified aerosol optical depth as the strongest model‐based contributor (mean absolute SHAP = 0.782), followed by the Tropical South Atlantic index (0.277). Higher observed AOD values were associated with positive SHAP contributions and higher predicted UTCI within the model. The machine‐learning framework identifies statistical associations rather than physical causality. Future work using aerosol speciation, radiative‐transfer analysis, and process‐based climate modelling is needed to clarify the underlying mechanisms. These findings highlight increasing thermal stress along Ghana's coastal‐urban corridor and suggest that aerosol–climate interactions deserve greater attention in tropical coastal heat‐stress research.
ABSTRACT Short‐term reservoir inflow forecasting is essential for flood‐risk mitigation and operational decision‐making in arid regions, where runoff is highly intermittent and dominated by short‐lived flash floods. This study compares persistence, multivariate linear regression and a Long Short‐Term Memory (LSTM) network for forecasting inflow to Najran Dam, Saudi Arabia, using hourly rainfall and reservoir inflow records for 2016–2020. Models were trained and evaluated using a strict chronological framework at lead times of 1, 2, 4 and 6 h. Forecast performance was assessed using root mean square error, mean absolute error and Nash–Sutcliffe efficiency. Forecast skill decreased rapidly with increasing lead time for all models. Linear regression consistently outperformed persistence beyond the 1‐h horizon, demonstrating the value of combining rainfall forcing with recent inflow observations. In contrast, the LSTM did not provide systematic improvement over the simpler and more interpretable linear‐regression model under the available 5‐year record. At the longer tested lead times, both approaches converged toward low skill, reflecting the combined influence of data sparsity, extreme inflow skewness, limited rainfall information and rapid catchment response. Useful forecast skill under the tested dataset, predictors and model configurations was concentrated within approximately 1–4 h. These findings emphasise the importance of benchmarking complex models against simple and interpretable alternatives when developing operational reservoir‐inflow forecasting systems from short hydrological records.
ABSTRACT This study investigates how different tropical cyclone (TC) track types modulate high‐temperature events in the Pearl River Delta (PRD) of China and how these effects may change under global warming, using observational and reanalysis datasets together with high‐resolution climate simulations. TC tracks are classified into four types: far‐ocean recurving, short‐track westward‐moving, long‐track northwestward‐moving and near‐shore recurving TCs. Results show that PRD temperature responses to TCs strongly depend on both track type and TC‐PRD distance. Short‐ and long‐track TCs exhibit clear distance‐dependent impacts, with warming at larger distances and cooling when approaching the PRD. Short‐track TCs produce the strongest warming at intermediate distances (800–1700 km), whereas long‐track TCs induce the strongest cooling within 800 km of the PRD. Near‐shore recurving TCs exert a warming influence during their westward‐approaching stage, whereas their eastward‐departing stage has no significant impact. Far‐ocean recurving TCs show limited direct influence on PRD temperature because they remain far from the region. These responses arise from track‐dependent mechanisms. Warming associated with short‐ and long‐track TCs is mainly driven by a westward‐extended subtropical high and enhanced large‐scale subsidence, whereas warming linked to westward‐approaching recurving TCs is dominated by TC‐induced subsidence on the western flank. Cooling during short‐ and long‐track TCs is mainly caused by cloud–radiative effects and precipitation‐induced cooling as storms approach the PRD. Under global warming, the frequency of all TC track types decreases, weakening both TC‐induced warming and cooling effects over the PRD. The combined impact of TCs nonetheless remains net cooling, although weaker in the future climate.
ABSTRACT The weather and climate of the tropical region are significantly modulated by the tropical intraseasonal oscillations. These oscillations are mainly constituent of convectively coupled equatorial waves (CCEWs) and Madden–Julian oscillation (MJO). Though a smaller region and surrounded by oceanic water, the country Sri Lanka faces various types of extreme weather conditions. The extreme rainfall event (ERE) is one of the major disasters and the influence of these tropical waves in the occurrence of extreme events cannot be overlooked. The modulation of EREs by these oscillations is analysed over Sri Lanka during two core monsoon and two inter‐monsoon seasons using very high‐resolution 12 × 12 km rainfall from Indian Monsoon Data Assimilation and Analysis (IMDAA) reanalysis from 1979 to 2020. The probability of ERE is calculated during dry and wet phases of the different waves relative to the climatological probability for all the seasons. These probabilities are not consistent and highly varying across the seasons over Sri Lanka. The modulation of extreme rainfall by the MJO and CCEWs is clearly evident during wet and dry phases of the waves, with a systematic increase (decrease) in the probability of extreme events during convectively active (suppressed) conditions. The results revealed that the wet phase of the equatorial Rossby (ER) and mixed‐Rossby gravity and tropical depressions together (MT) waves during northeast monsoon (NEM) season show maximum increase in the probability of ERE up to 150% while their dry phases reduced the probability of ERE by 100% during first inter‐monsoon (FIM) season over eastern parts of the country. It is important to note that these probabilities are relative to the climatological values. The probability of ERE reduced (increased) during dry (wet) phases for MJO and Kelvin waves during southwest monsoon (SWM) over western regions with lesser quantity. However, MJO has increased the probability of ERE over eastern parts during NEM season. To assess the broader impact over the country, the probabilities are spatially averaged and it is observed that the maximum probability of ERE occurs in the wet phase of ER followed by MT wave during NEM season. The wave filtered anomalies of vertically integrated moisture flux convergence (MFC) linked to the wet phases of the waves play a major role in modulating the ERE over the region.
ABSTRACT Drought poses a significant threat to agriculture in the transboundary Indus River Basin (IRB), which supports over 300 million people through direct and indirect dependence on agriculture. With limited cropland and growing climatic stress, the basin faces an increasing risk to food security. Yet, comprehensive assessments of cropland exposure to drought under a changing climate and socioeconomic conditions remain lacking. This study evaluates present and future cropland exposure to drought across the IRB using cropland data from the Land‐Use Harmonisation dataset (LUH2) and bias‐corrected output from seven CMIP6 global climate models, validated against ERA5 reanalysis data. We examined a baseline period (1995–2014) and three future time slices (2021–2040, 2041–2060 and 2081–2100) under seven Shared Socioeconomic Pathways (SSP1‐1.9 to SSP5‐8.5). Drought characteristics are quantified using the Standard Precipitation Evapotranspiration Index (SPEI) combined with run theory, incorporating CO 2 emissions. Results indicate that drought frequency is increasing in the future across the emission scenarios relative to the baseline. Cropland exposure displays pronounced spatial heterogeneity and a clear dynamic evolution over time, with the highest concentrations in the central IRB, particularly the Indo‐Pak Punjab and riparian corridors. By 2081–2100, annual cumulative exposed cropland area expands from 1875.3 to 2886.5 km 2 , representing an increase of 71.8%–88.1% relative to the baseline. Furthermore, attribution analysis shows that climate change dominates the increase in exposure through 2060, whereas cropland emerges as the primary driver by 2081–2100. These findings underscore the need for dual‐track drought risk‐management: climate‐focused adaptation for near‐term and integrated land‐use planning for long‐term resilience to safeguard food security in the IRB.
ABSTRACT Based on the Japanese 55‐year Reanalysis (JRA‐55) atmospheric circulation data provided by the Japan Meteorological Agency and monthly rainfall data from 28 meteorological stations across the Tarim Basin in Northwest China extracted by the China Meteorological Data Network from 1961 to 2022, the synergistic effects of the westerly and Asian monsoon on summer rainfall in the Tarim Basin are investigated. Results show that summer rainfall in the Tarim Basin has experienced a significant interdecadal increase over the past several decades and can be divided into two periods: A dry period (1961–1987) and a wet period (1988–2022). Under different climate backgrounds, the atmospheric circulations associated with the summer rainfall anomaly show obvious differences. The westerly indicated by the Silk Road Pattern (SRP) correlates well with the summer rainfall in the two periods, but the South Asian summer monsoon (SASM) (East Asian summer monsoon, EASM) is only well related to the summer rainfall in the dry period (wet period). In the dry period, the strengthened SRP and weakened SASM are associated with the southward movement of the subtropical westerly jet (SWJ) over Central Asia (CA). Meanwhile, the anomalous cyclone over CA and the anomalous anticyclone over the Arabian Sea strengthen the transport of water vapour from the Arabian Sea to the Tarim Basin. So, the Tarim Basin receives more summer rainfall. During the wet period, the strengthened SRP and weakened EASM as represented by the strengthened EAP atmospheric teleconnection can result in the SWJ moving northward over East Asia. Meanwhile, in conjunction with an anomalous anticyclone over the Mongolian Plateau, the westward extension of the West Pacific Subtropical High enhances the transport of water vapour from the Bay of Bengal to the basin. As a result, the Tarim Basin generates more summer rainfall.
ABSTRACT The anomalous summer melting of the Greenland Ice Sheet (GrIS) is one of the key signals of ongoing climate system changes. The complex spatiotemporal characteristics of GrIS and its impacts on global sea‐level rise have attracted increasing attention. Although previous studies have suggested that atmospheric blocking circulation can drive extreme melting events, the quantitative influence of subseasonal‐scale atmospheric processes on the total summer melt remains poorly understood. Based on a scale‐separation approach combined with Modèle Atmosphérique Regionale (MAR) simulations and satellite observations, this study provides the first systematic quantitative assessment of the contribution of subseasonal circulation processes to summer melting of the GrIS. While subseasonal processes account for an average of ~20% of the total summer melt, we find that a single pattern—Greenland Blocking (GB)—dominates this contribution. GB alone is responsible for up to 47% of the subseasonal melt, equating to approximately 10% of the total summer melt. The remaining subseasonal variance is driven by a spectrum of other circulation modes. To unravel the remaining subseasonal signal, we employ K‐means clustering to identify four distinct non‐blocking circulation patterns. However, their individual contributions are substantially smaller than that of GB. This study reframes the understanding of summer GrIS melt by highlighting the overwhelming dominance of blocking within the subseasonal framework.
ABSTRACT Climate change is driving shifts in the spatial distribution of climate zones worldwide, with particularly strong impacts in mid‐latitude regions. The Argentine Pampas, one of the world's most important agro‐productive areas, provides a key case for assessing these dynamics. This study analyses Köppen–Geiger climate transitions across four consecutive 30‐year periods (1901–1930, 1931–1960, 1961–1990 and 1991–2020) using high‐resolution maps and a pixel‐by‐pixel approach. Transitions were classified into nine categories, distinguishing single‐driver processes—changes in aridity, summer temperature or precipitation seasonality alone—from compound transitions in which precipitation seasonality and summer temperature shifted simultaneously within the same pixel. Results reveal that climate transitions affected between 8.6% and 14.3% of the regional area across consecutive periods, consistently exceeding global averages. The temporal trajectory is non‐linear, characterised by successive reversals in the dominant process: early 20th‐century aridity increase, mid‐century aridity reduction with the retreat of semi‐arid conditions and a recent dominance of winter drought development. Compound transitions remained spatially marginal in every period (0.5% or less), indicating that hydroclimatic and thermal shifts generally operated as spatially distinct processes across the region. In the most recent period (1961–1990 to 1991–2020), transitions affected 11.6% of the Pampas, with winter drought development accounting for over two‐thirds of all changes, primarily through shifts from Cfa to Cwa climates—a pure single‐driver hydroclimatic transition. Crop exposure analysis indicates that more than 3.1 million hectares of cropland fall within transition zones, with 91% concentrated in the winter drought development belt, encompassing the core soybean–maize–wheat production system. These results demonstrate that climate change in the Pampas is expressed primarily as a reorganisation of hydroclimatic regimes and precipitation seasonality rather than as uniform trends in annual conditions. The identified transition rates and spatial patterns position the region as a mid‐latitude climate change hotspot and highlight the need for territorially differentiated adaptation strategies in highly productive agricultural systems.
ABSTRACT Compound hot–drought events are high‐impact extremes that can amplify agricultural, ecological, and socioeconomic risks, yet their event‐scale detection and attribution‐oriented diagnosis remain limited for inland transitional regions. Here, we use Shanxi Province as a representative case of Central North China and develop a daily event‐based framework to identify warm‐season compound hot–drought events during 1961–2023 by combining the Meteorological Drought Composite Index with concurrent exceedances of daily maximum and minimum temperature thresholds. We further detect long‐term changes and abrupt shifts, identify major event years, verify representative events using disaster‐impact records, and diagnose associated circulation pathways through causal effect network analysis. The results show that both the annual number of compound hot–drought event days and the annual compound‐event percentage increased significantly during 1961–2023, with an abrupt shift around 1996–1997. Most top‐ranked event years occurred after this transition, indicating intensified compound hot–drought risk in recent decades. Event‐scale analyses identify 1997, 2001, and 2019 as representative major events, whose temporal evolution and spatial extent are consistent with documented drought impacts. Spatially, increasing trends are observed at most stations, and their consistency with hot‐day trends is much stronger than with drought‐day trends, suggesting a heat‐dominated intensification pathway. Causal effect network analysis reveals significant links between Shanxi compound hot–drought variability and the British–Baikal Corridor (BBC) and Silk Road Pattern (SRP) wave trains. Their coupled influence favours persistent anticyclonic anomalies, suppressed convection, and reduced precipitation over North China, providing a physically interpretable circulation pathway for major events. These findings establish an observational–impact–circulation evidence chain for attribution‐oriented diagnosis and provide a basis for future formal attribution and early warning of regional compound extremes.
ABSTRACT This study analyses dry‐hot winds, or sukhoviys , a recurrent phenomenon during the warm season across most regions of Ukraine. Sukhoviys promote rapid moisture loss from soil and vegetation, inducing acute stress on crops and intensifying drought effects during critical growth periods. Defining a sukhoviy as an event characterised by air temperature ≥ 25°C, relative humidity ≤ 30% and wind speed ≥ 5 m s −1 , we identified sukhoviy events over the period 1973–2025 using observational data from 34 meteorological stations. Using the Sukhoviy Station Index (SSI), we assessed the activity of sukhoviy events across Ukraine, taking into account their meteorological characteristics and frequency of occurrence. Based on a compiled catalogue of sukhoviy episodes and the Sukhoviy‐Day Index (SDI), the most intense events were identified with respect to duration and spatial extent. The largest number of sukhoviy days occurs in summer, particularly in July–August, averaging 3–4 days per month, with temperatures of 30°C–32°C or higher. Short episodes lasting 1–2 days are the most common, accounting for 67% of all cases. Prolonged episodes of 10 days or more represent approximately 3% of events; nevertheless, such episodes may simultaneously affect up to 16–19 stations. At several stations, an increase in sukhoviy days with temperatures above 30°C has been detected since 2007. Analysis of atmospheric circulation conditions shows a predominance of types associated with easterly air‐mass advection, with sukhoviys also frequently observed during southerly advection and the eastward extension of anticyclonic ridges from the west.
ABSTRACT This study provides a comprehensive assessment of meteorological, agro‐ecological, and hydrological drought dynamics in Türkiye over 1980–2022 using the standardized precipitation index (SPI) and the standardized precipitation evapotranspiration index (SPEI) at 3‐, 6‐, and 12‐month timescales. Trend magnitudes were quantified using Sen's slope estimator, and trend significance was evaluated using the Hamed‐Rao variance‐corrected Mann‐Kendall (M‐K) test. Temporal evolution was assessed using the sequential Mann‐Kendall (SM‐K) test and a 9‐point low‐pass Gaussian filter highlighting long‐period fluctuations. The Pettitt, Buishand, and Standard Normal Homogeneity Test (SNHT) tests were applied as change point tests, and persistence was evaluated using first‐order detrended fluctuation analysis (DFA). Results revealed a systematic divergence between SPI and SPEI series. While the SPI time series showed no statistically significant trends across annual, water‐year, or seasonal aggregations at the 3‐, 6‐, and 12‐month timescales, the SPEI time series showed statistically significant increasing drought trends across most annual, water‐year, and seasonal series. This indicates that precipitation‐only drought assessment may be incomplete in the case of Türkiye, where rising air temperatures amplify atmospheric evaporative demand and intensify drought‐prone climatic water‐balance conditions. Seasonal analysis indicated that significant SPEI trends concentrated in summer and autumn at the 3‐month scale expanded to include winter at the 6‐month scale and extended to all seasons at the 12‐month scale. SM‐K and Gaussian‐filter results indicated prolonged periods of statistically significant increasing drought trends mainly from the late 1990s and early 2000s onward, while the Pettitt, Buishand, and SNHT tests identified the change point in the SPEI series around 1998–1999. DFA scaling exponent ( α ) values approached or exceeded 1.0, especially for SPEI‐6 and SPEI‐12, indicating non‐stationary or low‐frequency‐dominant scaling within the fitted range. Regional assessments revealed that precipitation deficits were more evident in the inland semi‐arid regions, namely, Central Anatolia, Eastern Anatolia, and Southeastern Anatolia. In the humid coastal regions, increasing drought trends were more clearly expressed in the SPEI series that includes the evaporative demand. Overall, these results show a shift toward more persistent drought‐prone hydroclimatic conditions in Türkiye, particularly in the SPEI‐based climatic water‐balance series.
ABSTRACT As greenhouse gas concentrations continue to rise and the world enters uncharted climate conditions, climate projections at the regional scale are becoming more and more crucial for planning adequate adaptation and mitigation strategies. In particular, in mountainous regions, high spatial resolution of climate information is crucial, as climate variables often exhibit strong variability over short spatial scales. Here, we (1) present a novel downscaling method based on principal components analysis (PCA), (2) evaluate the influence of separating bias adjustment from downscaling, (3) compare several methods for downscaling, including quantile delta mapping and a lapse rate adjustment and (4) assess the impact of multivariate bias adjustment using a snowfall climate index. We test all our objectives on an ensemble of 11 regional climate models driven by reanalysis for the period 1989–2008 in a cross‐validation approach using two folds of 10 years. Our study area is Trentino‐South Tyrol, a mountainous region in the Southeastern European Alps. Here, we exploit an existing high‐resolution (1 km) gridded observational dataset for daily precipitation and temperature minima and maxima. Our results show that the novel PCA‐based downscaling method performs well, especially for precipitation and accurately captures the observed spatial patterns. Separating bias adjustment from downscaling allows for combining different approaches; however, if quantile delta mapping is used for both bias adjustment and downscaling, then the separation increases errors. Among different downscaling methods, quantile delta mapping performs best overall; however, for temperature, simple lapse‐rate adjustments also work reasonably well under average conditions. The multivariate bias adjustment shows minor benefits by accounting for inter‐variable correlations, especially when the climate models did not reproduce observed correlations between temperature and precipitation. Overall, this research provides novel insights for the post‐processing of climate model output in complex terrain, for example, when performing climate change impact models.
ABSTRACT Two atmospheric indices are introduced to improve the monitoring and early detection of El Niño–Southern Oscillation (ENSO) variability over Colombia: the Inter‐Basin Pressure Gradient Index (IPGI) and the Zonal Moisture Index (ZOMI). IPGI is constructed from monthly mean sea‐level pressure anomalies between the eastern tropical Pacific and the Caribbean Sea, while ZOMI is based on anomalies of vertically integrated horizontal moisture flux over Colombia. In both cases, the underlying fields are linearly detrended through the removal of the least‐squares linear trend, standardised, and low‐pass filtered with a second‐order Butterworth filter using a 1/12 cutoff frequency to isolate interannual variability. Using monthly data for the period 1981–2024, the indices are evaluated through cross‐correlation analysis, canonical correlation analysis (CCA), and Monte Carlo AR(1) significance testing to assess their statistical robustness and consistency with large‐scale ENSO variability. The results indicate that IPGI and ZOMI capture distinct but complementary modes of coupled ocean–atmosphere variability associated with the diversity of ENSO regimes across the equatorial Pacific. Cross‐correlation analyses show that the atmospheric indices systematically lead commonly used ENSO sea surface temperature (SST) indices by approximately 1–3 months, highlighting their potential as early atmospheric indicators. Autocorrelation‐aware significance testing reveals a scale‐dependent coupling structure: in the non‐prewhitened analysis the second canonical mode shows robust significance and reflects persistent low‐frequency coupled variability, whereas after prewhitening the leading ENSO‐related mode becomes statistically significant, indicating a more immediate SST–atmosphere interaction. Compared with traditional ENSO indices (ONI, MEI, SOI, Niño regions), IPGI and ZOMI provide earlier and more regionally relevant representations of ENSO‐related atmospheric variability, underscoring their value for climate monitoring, surveillance, and early warning applications over Colombia.
ABSTRACT This study evaluates seven climate extreme indices (CEIs) of precipitation and temperature and assesses their potential changes in 15 regions of the CORDEX‐CAM domain (from the southwestern United States to Central America) for different periods: historical (1981–2010), 2021–2040, 2041–2060 and 2080–2099. We used CHIRTS/CHIRPS and ERA5 reanalysis as references for analysing the CEIs and the historical performance of 17 Global Climate Models (GCMs) of the 6th phase of the Coupled Model Intercomparison Project (CMIP6). Future changes of the CEIs were examined under two Shared Socioeconomic Pathways (SSP: SSP2‐4.5 and SSP3‐7.0). The historical evaluation shows that a multi‐model ensemble (EnsGCMs) using the top 10 GCMs ranks second after the EC‐Earth3 model and the top 10 models are the same with the two references. The EnsGCMs reproduces the observed spatial patterns and the sign of the trends of the CEIs (positive for temperature and negative for precipitation in most regions), with some biases, especially extreme rainfall (R10mm and R95p). Interannual variation of precipitation indices is underestimated. By 2080–2099, TXx is projected to rise 3°C–4°C under SSP2‐4.5 and 4°C–6°C under SSP3‐7.0; TNn shows similar patterns, while DTR changes are small. The contribution of extreme precipitation above the 95th percentile (R95p) could increase between 10% to 30% in the far future (2080–2099), especially in subtropical regions amid a large uncertainty, while precipitation could decrease in tropical regions. This is the first study that analyzes future changes of CEIs using CMIP6 at regional scale in Mexico and Central America; it evidences the need for more regional climate information.
ABSTRACT East Africa is at risk of natural disasters caused by tropical cyclones (TCs) originating over the South‐West Indian Ocean (SWIO). Generally, TCs in the SWIO move westward or southwestward and often approach or make landfall over Madagascar and Mozambique, and previous studies have therefore tended to focus on these regions. However, the impact of TCs on Tanzania has received limited attention. Here, we investigated the TC‐related impacts on Tanzania in terms of rainfall variability associated with 29 TCs that approached Tanzania (Approaching TCs), the conditions favouring TC approach to Tanzania from the open SWIO, and the factors inhibiting TC genesis over the northern seas of Madagascar (NSM) during the period 1981–2024. The Approaching TCs brought substantially enhanced rainfall (+75% to +328%) over northern Mozambique, Tanzania, and south‐central Kenya relative to the mean precipitation during the rainy season. We revealed that the likelihood of TCs approaching Tanzania is low, but such Approaching TCs can produce heavy rainfall, potentially leading to flooding. The meteorological characteristics of TCs approaching Tanzania from the open SWIO include easterly steering winds extending south of the TCs, and equatorward shift of the Mascarene High to around 20° S. For TC genesis over the NSM, 850‐hPa absolute vorticity and the 500‐hPa meridional gradient of zonal winds play important roles. These factors are associated with the seasonal variability of the Mascarene High, including its equatorward extension and westward shift, which enhance low‐level easterly wind anomalies over the low‐latitude SWIO and modulate the dynamical environment for TC genesis over the NSM. These results provide useful insight for improving the understanding of TCs that affect Tanzania, and could potentially inform future efforts toward early warning or risk reduction of regional TC‐related disasters.
ABSTRACT Heatwaves have intensified across many regions due to ongoing warming, leading to significant environmental and societal impacts. The present study characterizes heatwave regimes across two contrasting subtropical regions of Mexico: the arid‐to‐semiarid northwest and the humid tropical Yucatán Peninsula. Using a station‐based percentile framework that enables consistent inter‐regional comparison, we identify marked differences in heatwave occurrence, intensity, and seasonal timing. In northwestern Mexico, heatwaves were characterized by higher peak temperatures occurring at a mean annual rate of approximately 0.16 events per year, with events clustering between June and August. In contrast, the Yucatán Peninsula exhibited a relatively higher occurrence rate (~0.33 events per year), with events concentrated between April and June before the onset of the rainy season. Station‐averaged event duration was concentrated in the 3–4‐day interval in NW and in the 4–5‐day interval in YP. These contrasts are further quantified through station‐based probabilities of heatwave occurrence derived from annual event counts, supporting the identification of two distinct heatwave regimes and providing a quantitative baseline for comparative climatological analyses and monitoring.
ABSTRACT Pakistan faces climate‐related hazards such as floods, droughts, and heatwaves, driven by diverse climate and rising temperatures. Reliable predictions are crucial for water management, agriculture, and disaster response, but the country's complex terrain requires region‐specific assessments. This study improves regional temperature projections by integrating ensembling techniques, statistical bias correction, and machine learning algorithms. Fuzzy C‐Means clustering divided Pakistan into six climatically similar regions. CMIP6 model outputs were evaluated and bias‐corrected to reduce errors. Temperature trends were estimated using both Sen's slope estimator and Random Forest regression (RFR). Sen's slope identified steady warming trends, while RFR captured nonlinear changes more effectively. Future projections under the SSP2‐4.5 and SSP5‐8.5 scenarios for three periods (2015–2044, 2045–2074, and 2075–2100) show significant warming, especially in central and southern Pakistan. Spatial analysis shows that warming increases relative to the baseline, notably under SSP5‐8.5, with larger increases in lowland and southern areas. The SSP2‐4.5 scenario suggests partial stabilisation toward the end of the century, while the SSP5‐8.5 scenario predicts accelerated warming that threatens food security, water resources, and public health. Bias correction reduces systematic deviations between modelled and observed data, but uncertainties persist under non‐stationary climate conditions. The framework improves regional analysis, yet uncertainties arising from bias‐correction assumptions and model limitations should be considered when interpreting results. By integrating these techniques, the study fills a gap in Pakistan's climate research, offering localised insights into future warming and valuable guidance for climate adaptation and policymaking.
ABSTRACT Evaluating the representation of observed precipitation trends in historical climate model simulations is challenging due to the combined effects of dynamical and thermodynamic changes and the strong influence of natural variability. Here, we assess the representation of long‐term (1901–2023) trends and variability in UK winter mean precipitation, focusing on the separation of dynamical and non‐dynamical components. We apply a dynamical adjustment methodology to the global climate models (GCMs) of the UK Climate Projections 2018 (UKCP18) to isolate circulation‐driven and non‐dynamical precipitation changes and variability. While several UKCP18 GCMs simulate a winter wetting trend broadly consistent with observations, the underlying drivers differ significantly. Modelled increases are primarily circulation‐driven, whereas observed trends are dominated by thermodynamic forcing. Also, the scaling of precipitation with temperature in ensemble members is substantially weaker than observed. While a thermodynamic signal emerges in the ensemble mean, its magnitude is less than a third of the observed rate. Consequently, these ensemble members may underestimate future non‐dynamical precipitation changes. Furthermore, the ensemble members under‐represent observed dynamical interannual variability, resulting in fewer high‐impact winters associated with persistent large‐scale atmospheric circulation anomalies. These results highlight important limitations in the representation of winter precipitation variability and change in the UKCP18 GCMs and have implications for the interpretation of model output in climate risk assessments and for the use of higher‐resolution ensembles within the UKCP18 framework.
ABSTRACT This study investigates how boreal winter equatorial stratospheric quasi‐biennial oscillation (QBO), defined by 70‐hPa zonal winds, modulates the El Niño–Southern Oscillation (ENSO)–South China Sea summer monsoon intensity (SCSSMI) relationship during 1952–2020 using the ERA5 reanalysis dataset. ENSO exhibits a strong correlation with the SCSSMI during the QBO westerly phase (WQBO), whereas their linkage becomes weak during the QBO easterly phase (EQBO). ENSO‐associated sea surface temperature (SST) evolution exhibits markedly different characteristics across contrasting QBO phases. From boreal winter to spring, WQBO suppresses deep convection over the Maritime Continent (MC) during El Niño events, yet this suppression is weak during La Niña events. This yields ENSO‐related Indo‐Pacific SST evolution analogous to that of canonical ENSO events, establishing a tight linkage between central–eastern equatorial Pacific SST in ENSO mature winter and the SCSSMI in subsequent ENSO decaying summer. However, EQBO substantially enhances MC deep convection and drives anomalous tropical zonal circulation over the Indo‐Pacific from winter to spring. Driven by these EQBO‐induced zonal circulation anomalies, anomalous vertical motion over the MC and anomalous surface winds across the Indo‐Pacific reshape ENSO‐related Indo‐Pacific SST evolution. During El Niño decaying summer, only weak warm SST anomalies emerge in the Indian Ocean; by contrast, significant cold SST anomalies prevail across the Indo‐Pacific during La Niña decaying summer. These anomalous SST patterns exert a negligible modulating effect on the SCSSMI, indicating that ENSO exerts a weak control on the SCSSMI during EQBO.
The North Atlantic constitutes the primary pathway for winter cyclones intruding into the Arctic, and these cyclones are typically much more intense than those formed locally. When extreme cyclones penetrate the Arctic, they transport water vapour and heat to the north, inducing pronounced anomalies in weather and sea ice. Based on objective trajectory analysis, three distinct types of extreme cyclones were identified according to their tracks from the North Atlantic to the Arctic: Type 1 and Type 2 cyclones intrude into the seas west and east of Greenland and the Arctic Ocean, respectively, while Type 3 cyclones migrate along the European continent margin. This research investigates the associated anomalies in Arctic atmospheric circulation and sea ice in response to these trajectories. The results reveal that the three types of extreme cyclones are linked to distinct circulation anomalies, characterised by polar vortex splitting, Arctic Oscillation, and polar vortex displacement. Furthermore, Type 1 and Type 2 cyclones favour warming from troposphere to lower stratosphere, whereas Type 3 cyclones are conducive to cooling in both troposphere and stratosphere. Preliminary analysis suggests that prior to and during the early stages of cyclone development, dynamic wind effects in the lower troposphere drive sea ice southward, reducing sea ice in the polar regions while increasing it in lower latitudes. Moreover, warm air advected northward by the cyclone promotes sea ice loss, whereas the intrusion of cold air in the cyclone's rear sector enhances sea ice formation.
