International Journal of Climatology

Several classical and innovative trend methods exist in the literature to identify and evaluate the effects of climate change on hydro‐meteorological variables. Among the classical methods, the most commonly used ones are modified Mann–Kendall (MMK) and Sen's slope (SS). As for the innovative methods to identify potential trends (probable risk levels) in hydro‐meteorological variables depending on changing the initial conditions and temporal dynamic development behaviour of the trends, the risk Sen's slope (RSS) method was proposed based on different risk values. The actual trends are proposed in this research to comprehensively understand and analyse the climate change trend over the entire period. It uses RSS and the classical trends MMK and SS. Also, the spatiotemporal classical, actual and potential trends in meteorological variables are evaluated. Additionally, the advantages of the RSS method compared with classical SS are discussed in detail. The Western Black Sea basin in Türkiye, with monthly total precipitation and monthly average temperature data from 1961 to 2023, is selected as a representative application. The temperature trend results show that the 0.99 risk level gave approximately 25% higher slope than SS. The maximum temperature‐increasing trend within the study area and the time period at 0.99 risk level is 2.10°C. However, the differences between precipitation trend slopes obtained by SS and RSS for different risk levels are relatively low. Furthermore, using different slopes corresponding to several risk levels allows for more proactive and effective measures for sustainable agricultural activities and water management. The actual temperature trend within the basin ranges between 1.33°C and 2.09°C, and the actual precipitation trend ranges between 2.78 and 12.74 mm over the study period.

This study focuses on identifying distinct precipitation zones across Europe, Spain and Catalonia, and second, examining how various large‐ and small‐scale climatic patterns affect the precipitation in these zones. Previous research has focused primarily on the relationships between individual climatic indices and precipitation in specific regions but has often overlooked the combined influence of multiple climate signals on precipitation variability. To address these issues, this study proposes the use of principal component analysis (PCA) as a multivariate analysis framework to investigate the complex relationships amongst multiannual precipitation patterns at different spatial scales, specifically in Europe, Spain and Catalonia. Distinct correlations amongst total annual precipitation occur in European countries, Spanish provinces and small Catalonian regions. Europe and Spain have five precipitation zones, whereas Catalonia has four. The calculated trends indicate a total precipitation reduction in the Iberian Peninsula, western Mediterranean and southwestern Europe, with a projected further decrease. Conversely, northern and central Europe anticipate normal to high precipitation tendencies. A second PCA application explores time and spatial correlations between precipitation zones and local/global climatic indices. The Southern Annular Mode, key Pacific teleconnections (PNA, TNA, WHWP, PACWARM and BEST) and confirmed Atlantic patterns (EA, NAO and AO) emerged as influential. The WeMO and MO indices showed the expected relevance at local spatial resolutions. Multivariate data analysis methods for two‐ or multidimensional datasets, which span multiple years and various spatial units (countries/provinces/regions), can extend the use of multivariate data analysis tools for correlation analysis over time in diverse geographical areas, including other continents, with varying spatial and temporal resolutions. The inclusion of monthly average precipitation data as an additional dimension in datasets analysed by multivariate statistical methods, such as PCA, will improve the knowledge of spatiotemporal climate variability.

Warm fronts often trigger significant weather changes, which also play a role in many extreme weather incidents. Therefore, it is crucial to understand the location and characteristics of warm fronts to accurately forecast weather changes. However, warm fronts are more difficult to identify than cold fronts on average, since the gradients can be weaker and shallower. This paper proposes a new objective method for identifying warm fronts in the Eurasia region using ERA‐5 hourly reanalysis data. The method uses the appropriate thermal front parameter and warm advection threshold to identify the potential warm frontal zone, then determines the corresponding warm boundary according to the predominant wind direction within the frontal zone, and finally locates the warm front line along the warm boundary. In various weather processes, the location and shape of the objective warm front correspond well with the distribution of weather systems and meteorological elements, indicating the effectiveness of the method. Meanwhile, the high agreement between objective and manual warm fronts further supports the reliability of the method. Furthermore, this method is applied to the long‐term datasets covering Eurasia, enabling an exploration of the climatological characteristics of warm fronts in the region. The study reveals distinct seasonal patterns in warm front frequency, with the highest frequency occurring during winter and the lowest during summer. Warm fronts are notably active in Europe, the Siberian Plain, the Northeast China Plain extending to the Western Pacific during winter, spring, and autumn. The dataset of warm fronts produced by this method proves valuable for climate change research.

The Bering Sea is undergoing major changes from increasing winter temperatures to the north, extreme minimum sea‐ice years in 2018 and 2019, through to an ecosystem reorganisation and negative impacts on communities' economic and subsistence food resources. These events have emerged under a global warming background, with positive feedback processes through a weakened atmospheric Alaskan Arctic Front (AAF) that promotes a self‐reinforcing process of sea‐ice loss, warmer air and sea temperatures, a wavy jet stream, and southerly winds. Interannual variability is still important: during 2021–2024 the Aleutian Low‐pressure system was regionally dominant in supporting a strong AAF, and sea‐ice conditions were observed close to the climatological mean. Before 2017, the AAF, consisting of cold dry air mass to the north and moist relatively warm air mass to the south, was a barrier to northward movement of storms, keeping the northern Bering/Chukchi seas with a cold Arctic climate. That historical situation is ending. Of critical importance is the probable reoccurrence of low Bering Sea sea‐ice years over the next decades and related ecosystem impacts. We propose that radically low sea ice will have a frequency of one to three 2018‐like low sea‐ice events per decade in the coming two decades, based on a historical meteorological analysis and ensemble climate model projections. Arctic temperatures to the north are increasing, weakening their contribution to the AAF. A weakened AAF and low sea‐ice years needs the winter Aleutian low pressure system to be far to the west of its average position, with southerly rather than northeasterly winds, warm years and low sea‐ice extent. From 1948 to 2024 meteorological records, this western location occurred with a range of zero to three times per decade. Communities need to plan for a response to intermittent occurrence of 2018‐like extreme sea‐ice loss and their ecosystem impacts over the coming decades.

Precipitation concentration represents the temporal unevenness of precipitation over a given period. A higher concentration increases the likelihood of concurrent flooding and drought. While previous studies have primarily focused on precipitation concentration at the daily scale, research on sub‐daily scales remains limited. Furthermore, the impact of temperature on precipitation concentration across various temporal scales is not well understood. In this study, we utilise high‐resolution precipitation products and the Gini index (GI) to examine the spatiotemporal characteristics of precipitation concentration across four different time scales (3, 6, 12‐h and 1‐day) over China. The climatological analysis reveals a gradual increase in precipitation concentration from southeast to northwest China. At shorter temporal scales (3 and 6‐h), Southeastern China exhibits a notable increase in precipitation concentration, while longer scales (12‐h and 1‐day) show a significant decrease throughout most regions of Northwest China. These observed spatiotemporal patterns are closely linked to temperature variations. At the 3‐h scale, precipitation concentration at the 3‐h scale increases with temperature at a nation‐averaged rate of 1.06% °C⁻¹ and decreases to 0.30% °C⁻¹ at the 1‐day scale. Higher temperatures intensify precipitation concentration at the 3‐h scale in Southeast China by increasing the frequency of heavy precipitation events. Meanwhile, in Northwest China, the decline in concentration at the daily scale under warmer conditions is attributed to increased annual precipitation amounts driven by higher temperatures. This study is of great significance, as it provides insight into how the temporal distribution of precipitation in China change under future global warming.

Rapid expansion (RE) of tropical cyclones (TCs) is a structural evolution that specifies the dramatic geometric synthesis increase in TC size. Its destructive potential is comparable or even more pronounced than that by the TC rapid intensification but receives limited attention. In this study, we utilise the ERA5‐derived 41‐year (1979–2019) global climatology of TC outer size data (i.e., effective azimuthal‐area‐average radius of 34‐kt gale‐force surface winds, R34EFF) to define RE and reveal the global climatology of RE for the first time, where RE is defined as the 90th percentile of global expanding samples (i.e., ΔR34EFF > 50 NM per 24 h; 1 NM = 1.852 km). Statistics show that 32% of all TCs underwent RE at least once during their lifetime. Climatologically, the proportion of RE decreased significantly in the globe (7%) and Northern Hemisphere (9%), particularly in the western North Pacific (8%). Seasonally, the RE proportion peaks in the early and late TC seasons. Spatiotemporally, distinct spatiotemporal variations and interdecadal changes of RE are found. In view of TC lifecycle, TCs likely reach their lifetime maximum intensity and lifetime maximum size after RE initiation. The duration of RE varies widely from basin to basin, while its seasonal variability is relatively smaller. Regarding the relationship between RE and TC intensity, the intensity of rapidly expanding TCs may increase or decrease with the former being more likely. The initial size and intensity of rapidly expanding TCs tend to be small (45 NM) and weak (60 kt), respectively. This study advances the understanding of RE from a global perspective, laying important groundwork for future study.

This study investigates the projected changes in the diurnal temperature range (DTR) over India and explains its considerable spatial heterogeneity from a 20‐km resolution coupled regional climate model (RSM‐ROMS) integration. The RSM‐ROMS is driven at the lateral boundaries by the Community Climate System Model version 4 (CCSM4) model. Observations reveal spatial heterogeneity in DTR trends with significant declining trends at many grid points interspersed with areas of either increasing or insignificant trends of DTR during each of the four seasons. The present‐day simulations from RSM‐ROMS show reasonable skill in simulating the daily maximum temperature (Tmax) and minimum temperature (Tmin) over India. Our results show a significant decrease in DTR over the Gangetic Plains in boreal winter and fall seasons and over southeastern India during boreal summer in the projected mid‐21st century climate under the RCP 8.5 emission scenario. The future reduction in DTR over Region‐1 (over Bihar and the eastern regions of Uttar Pradesh) during December–February (−0.86°C) and over Region‐3 (over the rain shadow regions of Peninsular India) during June–September (−0.49°C) is attributed to large changes in surface radiative fluxes, with some of the decrease in downward short wave flux attributed to an increase in high cloud cover at the time of Tmax while there is a considerable increase in downward longwave flux in the mid‐21st century climate. The enthalpy fluxes at the time of Tmax also act to reduce the rate of its warming. As a result, the warming rate of Tmax is less compared with the corresponding warming rate of Tmin, which leads to a reduction of the DTR in some regions that display a significant reduction in future climate. In contrast, Region‐2 (over Rajasthan) and Region‐4 (over northeast India) exhibit insignificant DTR changes in the mid‐21st century climate for lack of asymmetrical changes in Tmin and Tmax.

The Indo‐Pacific warm pool (IPWP), enclosed by a 28°C isotherm, is vital in controlling atmospheric circulations affecting monsoonal flow. The warming trend of sea surface temperatures (SSTs) over the IPWP has expanded the IPWP region. This study examines the impact of the IPWP warming on the Indian summer monsoon rainfall (ISMR) patterns using ERA5 reanalysis and India Meteorological Department rainfall records based on station data from 1959 to 2021. Analyses based on correlation, regression and composite anomalies show the complex relationship between recent decades of IPWP expansion/warming and monsoon circulation. However, the effects of regional IPWP SST warming changes on the ISMR pattern remain unexplored. Here, we explore the changes in the monsoonal circulation owing to the warming and expansion of IPWP, by comparing two equal periods (1959–1989 and 1990–2021). The responses of monsoons to IPWP warming in these two periods revealed some interesting facts, but the complexity remained. Further, we examined the composite impacts of IPWP SST warming in three categories, that is, very cool, usual and extremely warm, on the dynamics of monsoon circulations. The very cool IPWP is associated with the dry monsoon, while the extremely warm IPWP produces copious rainfall over southern India and dryness over eastern north India. The study confirms the non‐linear relationship between IPWP warming and ISMR, which has been investigated in detail.

Based on Coupled Model Intercomparison Project phase 6 (CMIP6) simulations, we found that the frequency and intensity of daytime–nighttime compound hot extremes (HEs) in the mid‐high latitudes of Asia (MHA) are expected to increase. The most significant increase is anticipated under the shared socioeconomic pathway (SSP) 5‐8.5, while the smallest increase is expected under SSP1‐2.6. Notably, unlike the decreasing trends of independent HEs since 2050 under the high emission scenarios, the compound HEs, which comprise the largest proportion, are expected to continuously increase and intensify. To better understand the impact of these changes on human society, we also focused on changes in population exposed to HEs. The findings reveal that population exposure to compound and nighttime HEs is projected to increase most rapidly under SSP3‐7.0, with estimates indicating increases of 10.06 and 3.80 times, respectively, by the end of the century. The most significant increases are expected in the mid‐latitudes, where changes in HEs are most pronounced. Climate change is the primary driver behind the rising population exposure to compound and nighttime HEs, with its impact expected to grow over time. Conversely, exposure to daytime HEs is primarily influenced by population changes, particularly in urban areas. Therefore, effective climate change mitigation and adaptive strategies are crucial to reducing future population exposure to HEs in MHA.

Urban areas experience the impact of natural disasters, such as heatwaves and flash floods, disparately in different neighbourhoods across a city. The demand for precise urban hydrometeorological and hydroclimatological modelling to examine this disparity, and the interacting challenges posed by climate change and urbanisation, has thus surged. The Weather Research and Forecasting (WRF) model has served such operational and research purposes for decades. Recent advancements in WRF, including enhanced numerical schemes and sophisticated urban atmospheric‐hydrological parameterizations, have empowered the simulation of urban geophysical processes at high resolution (~1 km), but even this resolution misses significant urban microclimate variability. This study applies the large‐eddy simulations (LES) mode within WRF, coupled with single‐layer urban canopy models (SLUCM), to enable even finer‐scale modelling (150 m) of the Urban Heat Island (UHI) effect in the Baltimore metropolitan area. We run nine scenarios to evaluate various methods of initializing soil moisture and various spinup lead times, and to assess the impact of WRF's Mosaic approach in depicting subgrid‐scale processes. We evaluate the scenarios by comparing the WRF simulated land surface temperature (LST) against Landsat LST and the WRF simulated hourly 2‐m air temperatures (AT) with observations from eight weather stations across the domain. Results underscore the paramount influence of the lead spinup time on the spatiotemporal distribution of simulated soil moisture, consequently shaping WRF's efficacy in predicting the UHI. Furthermore, interpolating soil moisture‐related parameters from the parent for child domain initialization yields a notable reduction in mean and root‐mean‐squared errors. This improvement was particularly evident in simulations with the longest spinup time, affirming the importance of carefully designing the initialization of soil moisture for improved urban temperature predictions.

Sea surface temperature (SST) is a significant climatic variable that affects the climate of the Earth. Monitoring a location's SST pattern is useful for several research areas, including weather forecasting and climate change. In this study, the emerging hot spot and cold spot patterns of SST in the Mediterranean and Black Sea Marine System (MBMS) were examined, the spatial distribution characteristics and temporal changes of SST in the sub‐basins were analysed, and future predictions were made. A distinctive aspect of the research lies in the introduction of novel techniques, specifically the application of space time cube and evolving hot spot analysis, for visualising and evaluating SST in the MBMS. This approach sets the study apart by pioneering the utilisation of these methods in this particular context. In the examined region, SST demonstrates a decreasing trend from east to west and from south to north. The forecast suggests that this spatial distribution pattern will persist in 2033, further accentuated by the intensification of the warming effect. Nine different time series clusters are defined within this distribution pattern. Although it changes seasonally, the prevailing statistically significant hot spots in the study area are primarily characterised by new hot spots, intensifying hot spots, sporadic hot spots and oscillating hot spots. The trends of hot and cold spot clusters, along with SST values, were assessed for all sub‐basins in the MBMS. Conversely, the observed clustering category among statistically significant cold spots is identified as persistent cold spots, diminishing cold spots, sporadic cold spots, oscillating cold spots and historical cold spots. The spatiotemporal analysis in this research has provided notable insights, offering a spatial context to the previously explored temporal trends of SST in the MBMS.

Regional cold winters have occurred frequently in Eurasia since the beginning of the 21st century, increasing the interannual variability in winter temperatures and increasing the difficulty of prediction. In this study, we evaluate the performance of Climate Forecast version 2 (CFSv2) of the National Centers for Environmental Prediction (NCEP) in predicting winter temperature anomalies over the Northern Hemisphere and find that CFSv2 has significantly lower temperature prediction ability for cold winters in the mid–high latitudes of Eurasia since the 21st century. This is mainly due to the stronger response to global warming and the weaker response to sea ice anomalies in the preceding autumn in CFSv2 than the in reanalysis. Accordingly, two targeted correction methods have been developed to improve the prediction ability, with the first method removing the linear temperature trend of CFSv2 predictions and the second method considering the effects of autumn Arctic Sea ice anomalies via a dynamical–statistical correction approach (DSCA). Both methods can effectively improve the prediction ability of winter temperature anomalies in the mid–high latitudes of Eurasia, especially in cold winters. The anomaly correlation coefficient (ACC) increased from −0.03 to 0.13 before and after the modification by the DSCA, and from −0.12 to 0.25 for cold winters. The DSCA significantly reduced the root mean square error (RMSE) of the CFSv2 predictions by approximately 10%.

Current East Asian winter monsoon (EAWM) indices effectively depict the associated high‐ and low‐latitude atmospheric circulations. However, the spatial dynamics of the winter coldness within the monsoon domain are not well adequately represented by EAWM indices. We introduce a novel approach to classify winter temperatures based on both their co‐variability and their mean values. We classified the EAWM domain into three distinct modes: northern (ranging from −27°C to −15°C), central (−14°C to 5°C), and southern (6°C to 27°C). The northern mode, characterised by intense coldness, correlates with a strengthened westerlies that traps Arctic cold air masses during the positive phase of the Arctic Oscillation (AO). In contrast, the southern mode is primarily influenced by low‐latitude oceanic and atmospheric patterns, particularly for near‐coast areas. The central mode, representing an interplay of both high and low‐latitude processes, encapsulates the comprehensive characteristics of the EAWM. Our analysis reveals a notable shift in the relationships among the northern, central, and southern modes around 1990. Prior to this year, the EAWM was predominantly influenced by northern atmospheric patterns, while there is a discernible increase in the influence of low‐latitude drivers afterwards. This shift may be linked to the significant warming in the western Pacific and Indian Oceans, underscoring the heightened role of low‐latitude drivers on the EAWM.

Climate extreme events are intensifying globally, posing increasing risks across various sectors. Understanding climate extremes' spatiotemporal patterns and responses to climate change is crucial for effective management, especially on a regional scale. This study examines temperature and precipitation extremes, as well as compound dry‐hot events (CDHEs), in the Ishikari River basin (IRB) of Northeastern Japan, an area of significant socioeconomic importance. We focus on spatiotemporal analysis under multiple scenarios of temperature/precipitation extremes and CDHEs based on statistical downscaled datasets from the Coupled Model Intercomparison Project Phase 6. Results indicate that IRB underwent increased trends of extreme hot periods, extreme droughts, and heavy rainfalls during 1985–2014, which are significantly affected by the North Pacific Oscillation and Southern Oscillation Index. Future projections show that warming temperatures and less rainfall shift asymmetrical impacts on temperature and precipitation extremes, expecting increased warm spells and CDHEs but increased wet durations and less heavy rainfalls. Emission scenarios analysis suggests low‐emission scenarios (SSP1‐2.6) could mitigate their exacerbations, especially for CDHEs (decreased by 139%). Moreover, spatial‐pattern analysis reveals regional heterogeneity in temperature and precipitation extremes, with northern mountainous regions more susceptible to thermal extremes and southern plain regions (e.g., Sapporo city) experiencing prolonged drought and CDHEs. This study provides valuable insights into climate risk management and adaptation strategies.

This paper presents a detailed spatio‐temporal analysis of the rainfall in the state of Pernambuco, Northeast Brazil. It is based on climate indices for extreme precipitation recommended by the Expert Team on Climate Change Detection, Monitoring and Indices. To accomplish this, daily rainfall 1data (1961–2019) were extracted from 809 high‐resolution grid points (0.1° × 0.1°) using the Brazilian Daily Weather Gridded Data (BR‐DWGD). The significance and magnitude of index trends were assessed using the modified Mann–Kendall and Sen's slope tests. This study also examined whether there existed a significant difference in climate indices among the three regions (Sertão, Agreste and Zona da Mata) within the state. The findings revealed notable significant negative trends in the PRCPTOT, R10mm, R20mm, Rx1day, Rx5day and CWD indices across all regions of Pernambuco, exhibiting a gradient from the coast to the state's interior. Reduction values of up to 15 mm year⁻¹ for PRCPTOT, 0.7 day year⁻¹ for R10mm, 0.2 day year⁻¹ for R20mm, 0.01 mm year⁻¹ for Rx1day, 0.03 mm year⁻¹ for Rx5day, 0.4 day year⁻¹ for CWD were observed. Furthermore, an alarming pattern was also noted for CDD, displaying a higher concentration of significant positive trends in all regions of the state, with estimated increases of up to 1.4 day year⁻¹. Conversely, a balance of trends—both positive and negative—was observed across the entire state for R95p and R99p, with a majority of trends proving non‐significant. SDII exhibited a higher frequency of grid points showing a significant positive trend, particularly notable in the Sertão and Zona da Mata regions, where significant differences in the index values were absent. However, the remaining indices showcased notable regional differences, with values decreasing from the east to the west of the state, except for CDD. This study will assist decision makers, providing detailed long‐term information essential for preventing natural disasters and supporting socioeconomic and environmental policies in the state.

In the context of global warming, the rise in extreme precipitation events in high‐altitude headwater areas has introduced greater hydrological uncertainty. However, the limited understanding of the physical mechanisms driving extreme precipitation in these areas hinders efforts to mitigate the potential rise in future precipitation risks. This study analysed the extreme precipitation events in the headwater area of the Yellow River (HAYR) from May to September each year from 2015 to 2020 using satellite‐based data from Dual‐frequency Precipitation Radar (DPR) on the Global Precipitation Measurement (GPM) Core Observatory and Integrated Multi‐satellite Retrievals for GPM (IMERG). The results show that stratiform precipitation (SP) determines the spatial extent of extreme precipitation events, while convective precipitation (CP) largely affects the rainfall intensity. Statistical analysis from different extreme precipitation events indicates that the rain rate of CP is 2 to 3 times higher than that of SP, thus zones of intense precipitation in the study area are normally dominated by CP. Vertically, the topographic lifting in complex mountainous regions exerts opposite effects on the precipitation rates of SP and CP, weakening the precipitation intensity of SP while enhancing that of CP. The peak precipitation rate in the midstream and downstream regions is observed at approximately 5 km, whereas the upstream region displays a distinctive double‐peaked distribution, with one peak at 8.5 km and another near the surface. This study provides a better understanding of the interior structure evolution process of plateau precipitation, as well as the associated microphysical properties, and highlights some insights to improve microphysical parameterization in the future model developments.

The Fire Weather Index (FWI) is a widely used metric to estimate the wildfire risk based on climatological variables. As anthropogenic climate change is expected to increase wildfire risk by affecting the climate of the Mediterranean Iberian Peninsula, we assess the expected increase in wildfire risk during the past decades. For this purpose, we employ a dataset containing daily FWI values in a 0.25° × 0.25° grid for each day of a 52‐year period, between 1971 and 2022, and perform a trend analysis at a statistically significant level. We evaluate the relation between FWI and spatial (altitude, latitude, and distance to the sea) variables to look for significant correlations. An analysis is performed at the geographic level by focusing on changes in concrete, relatively homogenous zones (subregions) to broadly study spatial patterns of change. The most relevant results are (1) the FWI shows an increasing trend across the study area (0.01 confidence level); (2) the FWI is determined by temperature variations on a multiyear scale, but annually by more volatile precipitation patterns; (3) the FWI does not uniformly behave across either space or time, and is subject to different variations in different zones; (4) summer and winter are the seasons with the most significant increase, and autumn is the only not significant season; (5) very high or extreme risks are increasingly prevalent across the territory, increasing wildfire risk and (6) the FWI more rapidly rises in areas further north, at a longer distance to the sea and at higher altitudes, with the Iberian System being the most affected region. The increase in wildfire risk requires putting in place more preventive measures. Our study results coincide with climatological trend studies on the region and bridge a knowledge gap as regards the historical climatology of the FWI.

As global surface temperatures have increased with human‐induced climate change, notable compound climate extremes in the New Zealand (NZ) region associated with atmospheric heatwaves (AHWs) and marine heatwaves (MHWs) have occurred in the past 6 years. Natural modes of variability that also played a key role regionally include the Interdecadal Pacific Oscillation (IPO), El Niño/Southern Oscillation (ENSO) and changes in the location and strength of the westerlies as seen in the Southern Annular Mode (SAM). Along with mean warming of 0.8°C since 1900, a negative phase of the IPO, La Niña phase of ENSO and a strongly positive SAM contributed to five compound warm extremes in the extended austral summer seasons (NDJFM) of 1934/35, 2017/18, 2018/19, 2021/22 and 2022/23. These are the most intense coupled ocean/atmosphere (MHWs/AHWs) heatwaves on record with average temperature anomalies over land and sea +0.8°C to 1.1°C above 1991–2020 averages. The number of days above 25°C and above the 90th percentile of maximum temperature has increased, while the number of nights below 0°C and below the 10th percentile has decreased. Coastal waters around NZ recently experienced their longest MHW in the satellite era (1982‐present) of 289 days through 2023. The estimated recurrence interval reduces from 1 in 300‐years for the AHW event during the 1930s climate to a 1 in 25‐year event for the most recent decade. Consequences include major loss of ice of almost one‐third volume from Southern Alps glaciers from 2017 to 2021 with rapid melt of seasonal snow in all four cases. Above‐average temperatures in the December/January grape flowering period resulted in advances in veraison (the onset of ripening); and higher‐than‐average grape yields in 2022 and 2023 vintages. Marine impacts include widespread sea‐sponge bleaching around northern and southern NZ.

The impact of extreme events in risk analysis depends on factors such as magnitude, duration, timing and whether the system recovers fully before the next event occurs. While previous studies have primarily examined the drivers and characteristics of individual extremes, less focus has been given to the concurrent or compounding nature of extremes across adjacent seasons. Thus, understanding the dynamics of such compound extremes, particularly dryness and wetness, is crucial. To address these concerns, a Multi Scalar Drought Index (MSDI) is formulated using precipitation and temperature data from three river basins (Brahmani, Baitarani and Cauvery) of eastern and southern India. The combinations of dryness and wetness, such as Dry‐Dry, Dry‐Wet, Wet‐Dry and Wet‐Wet, between consecutive seasons are analysed across four seasons (summer, rainy, autumn and winter). The prolonged dryness/wetness along with dry/wet year are evaluated from baseline (1979–2018) to projected COrdinated Regional Climate Downscaling Experiment (CORDEX) future period (2020–2099). The spatio‐temporal variations in intra‐annual dry‐wet extremes are identified using the Mann–Kendall test. The results suggest that the eastern Indian river basins, particularly the Brahmani and Baitarani basins, experience more frequent occurrences of compounding dryness‐wetness compared to Cauvery river which is a southern Indian basin. Future scenarios indicate a trend towards dryness during the monsoon season in Brahmani and Baitarani basins, with frequent wet extremes in late autumn and winter. Abrupt transitions between dryness and wetness are prevalent during the Rainy‐Autumn and Autumn‐Winter seasons in Brahmani and Baitarani basins. The increased frequency of compound dry‐wet extremes poses significant socio‐economic risks, including reduced agricultural productivity, water management challenges and heightened vulnerability of local livelihoods dependent on consistent water availability. The results of this study provide a scientific reference for sustainable agriculture and water resource management to predict future seasonal dry and wet alternations and develop effective mitigation strategies.

Global warming has significantly increased the frequency and intensity of extreme events, which have catastrophic consequences for ecosystems and humans. Despite efforts to assess the impact of climate change on the potential risk of Borneo, most research has focused on partial regions, considering short timescales and a limited number of temperature and precipitation extremes indices to quantify the expected climate risks. This study employed a new method of climate risk assessment of Borneo based on the combined changes in various climate parameters. It estimated 23 climate indices at all grid points covering Borneo for three overlapping sub‐periods (1951–1980, 1961–1990, 1991–2020). The modified Mann‐Kendall test was employed to identify grid points exhibiting significant increasing or decreasing trends of each index for each sub‐period. Finally, significant trends of 23 indices were integrated to estimate the potential climate risk indicator (RI) based on the combined changes in various climate parameters for each grid point and sub‐period. Temperature indices showed a clear warming trend across Borneo Island, particularly in the eastern regions, with absolute temperature indices showing an increase of 0.5°C–2.5°C in 1991–2020 compared to the reference period (1951–1980). However, extreme cold temperatures have become less prevalent over the study period. There is a shift from light consecutive rainfall days towards more heavy and short‐duration rainfall events. Therefore, there are indications of intensifying rainfall events over the island's southern half, counterbalanced by drying trends in the northern regions, especially Brunei. The spatial distribution of RI revealed an overall 184% increase in climate risk on the island in recent years (1991–2020) compared to the reference period. The highest rise in RI was in the central east of the island, mostly due to significant increases in rainfall and temperature indices. The findings can inform adaptation initiatives to manage escalating heat and flood risks while guiding additional research to explain further the complex climatic changes occurring in this ecologically and socially vital region.

The anomalous variability of extreme cases of the tropical tropopause provides insight into the stratosphere‐troposphere exchange process crucial for understanding climate change. The present study analyses the extreme variability of the tropopause and its thermal structure over the tropics using GPS radio occultation data over the period 2006–2019. The extremely cold and warm tropopauses and extremely high and low tropopauses are identified based on the cold point tropopause temperature and height, respectively, when their values exceed two standard deviations with respect to their climatological means. The analyses revealed frequent occurrence of extreme cases of tropopause over the Atlantic Ocean compared to the Western Pacific Ocean. Individually, extremely warm and low cases occur more frequently over the subtropics, while extremely cold and high cases occur frequently over the deep tropics. These extreme cases pose different thermal structures bounded within the extremely low and high tropopauses throughout the tropics. The height difference between the extremely high and low tropopause cases is wider over the Atlantic Ocean and adjoining areas compared to the western Pacific Ocean. The temperature difference between the extremely warm and cold tropopause cases is higher in the Atlantic, Central Pacific, and Indonesian regions compared to the American, Indian Ocean, and western Pacific Ocean regions. The relationship between the El Niño Southern Oscillation (ENSO) phases and extreme tropopause cases is also investigated which reveals a higher occurrence of extremely high tropopause cases during the El Niño phase while low tropopause cases during the La Niña phase. Our analysis also revealed the thermal patterns of the extreme cases characterising colder and sharper tropopause over the convective regions compared to subsidence regions.

Prediction of seasonal onset is crucial to agriculture in southern and eastern Africa. Here, we applied two definitions of onset, namely meteorological and agricultural (crop‐germination), to evaluate CMIP6 models through the lens of rainfall onset over representative maize agricultural regions of South Africa, Tanzania, Malawi and Zambia. We use the ERA5 reanalysis as a proxy for observations, and robust regression to calculate a statistical comparison of the onset definitions for the period 1979–2021. Evaluation of ERA5 reanalysis shows similar magnitude and pattern as gauge based MSWEP. Our results show that, for meteorological onset, Johannesburg, with a subtropical highland climate, experienced earliest onset after 23 December; and an increasing trend (later onset) but not statistically significant (p = 0.2). Over Bethlehem, which has continental climate, the earliest onset date was after October 9 and an increasing interannual variability since 2000 is noted. The standard deviation of onset dates across the regions shows an East‐Central‐South gradient. We also found that the crop‐germination onset definition shows earlier onset of seasonal rains, it differs considerably across regions, and has higher interannual variability, in comparison with the meteorological definition. Over Lilongwe, Mbeya and Lusaka, late meteorological onset with a weak positive and insignificant trend is observed. The CMIP6 model's representation of onset trend differs from reanalysis data, with inter‐model differences. Late meteorological onset is underestimated by GFDL‐CM4 and MPI while INM5, MPI and NorESM overestimate the observed earliest onset. The largest bias is shown by INM and MPI which simulate earliest and latest onset as 190 (07 January) and 206 (23 January) respectively. In addition, models often fail to simulate sufficient precipitation to produce onset for seed germination and crop development. The ACCESS model showed an insignificant trend (p value = 2) and later onset over Lilongwe, an insignificant trend (p value = 0.9) over Lusaka, and an earlier onset over Mbeya. Using the agricultural onset definition, over Bethlehem, all the models and the ERA5 reanalysis did not produce enough precipitation to meet onset conditions. We suggest that rainfall onset studies use several definitions or metrics of onset and that the choice of metric be informed by the research question. Using such an ensemble of onset metrics contributes to a better understanding of variability and uncertainties in agricultural productivity.

Precipitation changes dynamically in the Mediterranean region. Therefore, the projection of future precipitation and its historical distribution mechanism is essential for climate mitigation and adaptation. In this study, a stepwise clustered precipitation downscaling method (SCPD) was developed and adopted in the Mediterranean region to reveal the inherent variation rules and trends over the future 100 years under two SSP scenarios. A cutting and merging multivariate process is introduced to build a cluster tree for supporting further downscaling and projecting steps. The ensemble average from the global climate model (GCM) dataset is used for precipitation projections. The precipitation performance of SCPD, evaluated by R², is fairly decent. The precipitation projections vary with the original rainfall patterns over the gauge stations. Dry places tend to become comparably drier in the future. Precipitation in the northern Mediterranean region shows a drier winter–spring and wetter summer–autumn. Opposite trends emerged in the southern part, with increasing winter precipitation and decreasing summer rainfall. The rising carbon dioxide concentration will further intensify the decrease in rainfall. However, the centres of these two EOFs are not identical. The contributions of NAO (positive) and Niño 3.4 (negative) to PC1 are relatively high. Accordingly, the strongest positive correlation with PC2 is SCAND, as well as negative correlations with AO, NAO and EAWR. Positive anomaly precipitation is attributed to PC1, whereas PC2 is responsible for most of the negative variance precipitation.

The reliability of seasonal snow cover information is constrained by limitation of in situ observations and uncertainties in remote sensing data and model simulations in alpine region, thus posing important challenges to understanding the climate system and water resource management in alpine region. Here, the assimilation of daily cloud‐free Moderate Resolution Imaging Spectroradiometer (MODIS) normalised difference snow index (NDSI) product into an intermediate complexity snow mass and energy balance model—Flexible Snow Model (version FSM2_MO)—was implemented. The aim is to improve the model simulations of seasonal snow cover (snow‐covered extent; SCE, snow depth; SD, snow water equivalent; SWE, and snowmelt runoff; SMR) in the alpine region (a case of the upper‐middle reaches of the Heihe River basin, Northwest China). The results indicate comprehensive improvement in the simulation of SCE, SD, and SMR in the study area through data assimilation, with the ability to significantly reduce prior biases of the FSM2_MO. Based on the independent daily cloud‐free Advanced Very High Resolution Radiometer (AVHRR) SCE product, the updated SCE simulation (i.e., data assimilation) showed a reduction in mean absolute error (MAE) from 10.46% to 7.16%, root mean square error (RMSE) from 16.14% to 12.26%, and an increase in Pearson's correlation coefficient (CC) from 0.18 to 0.67 compared with the open loop simulation (i.e., without assimilation). The evaluation results of SD observation data showed that data assimilation improved SD simulation compared with the open loop run (OL). And utilising the monthly discharge observations at the Yingluoxia hydrological station, data assimilation slightly improved the SMR simulation. The updated SMR simulation achieved a CC of 0.91, Nash‐Sutcliffe efficiency coefficient (NSE) of 0.73, and Kling‐Gupta efficiency coefficient (KGE) of 0.76. Moreover, the Landsat 8‐derived snow cover map and Sentinel‐1‐derived SD also indicated that the updated simulation effectively filled in the missing snow cover and removed the superfluous snow cover predicted by the OL simulation in terms of spatial distribution.

Foehn winds, characterised by abrupt temperature increases and wind speed changes, significantly impact regions on the leeward side of mountain ranges, e.g., by spreading wildfires. Understanding how foehn occurrences change under climate change is crucial. As foehn is a meteorological phenomenon, its prevalence has to be inferred from meteorological measurements employing suitable classification schemes. Hence, this approach is typically limited to specific periods for which the necessary data are available. We present a novel approach for reconstructing historical foehn occurrences using a combination of unsupervised and supervised probabilistic statistical learning methods. We utilise in situ measurements (available for recent decades) to train an unsupervised learner (finite mixture model) for automatic foehn classification. These labelled data are then linked to reanalysis data (covering longer periods) using a supervised learner (lasso or boosting). This allows us to reconstruct past foehn probabilities based solely on reanalysis data. Applying this method to ERA5 reanalysis data for six stations across Switzerland and Austria achieves accurate hourly reconstructions of north and south foehn occurrence, respectively, dating back to 1940. This paves the way for investigating how seasonal foehn patterns have evolved over the past 83 years, providing valuable insights into climate change impacts on these critical wind events.