Coastal populations in Southeast Asia are highly vulnerable to climate change impacts. This systematic review synthesizes peer-reviewed literature from 2020-2025 retrieved from Web of Science, Scopus, and Google Scholar, focusing on climate change adaptation strategies in 10 Southeast Asian coastal countries. Eligible studies (n = 51) were analyzed through thematic coding, identifying 104 distinct adaptation strategies across 7 themes: ecosystem-based adaptation (21%), livelihood diversification (19%), early warning systems and disaster preparedness (16%), climate-resilient agriculture and fisheries (14%), infrastructure-based adaptation (11%), marine resource management and co-governance (10%), and traditional and indigenous practices (9%). Key adaptation strategies included mangrove restoration (reduces erosion and storm surges), sustainable aquaculture, disaster preparedness, and livelihood diversification (mitigates income loss from saline intrusion). Ecosystem-based and livelihood diversification approaches improve ecological stability, financial security, and community adaptive capacity, strengthening overall resilience. However, their long-term sustainability remains uncertain due to limited long-term evaluations, insufficient cost-effectiveness analysis, and weak alignment with national policies; in fact, most initiatives remain project-specific with limited evaluation of long-term effectiveness or cost-efficiency. Furthermore, equity considerations such as gender, poverty, and resource access are insufficiently integrated, potentially excluding marginalized groups. A critical gap persists between community-led innovations and national or regional adaptation frameworks, raising questions of scalability, funding, and policy alignment with national adaptation plans and nationally determined contributions. This review identifies the need for inclusive, equity-focused, and longitudinal research that links grassroots strategies with governance structures, ensuring sustained climate resilience for Southeast Asian coastal communities.
This study explores the connection between key meteorological variables and malaria incidence across Africa, addressing a critical gap in understanding how climate patterns influence disease persistence. Employing Climatic Research Unit gridded time series (CRU_TS) climate data (1981-2024), we established the K Malaria Index (KMI), an integrated quantity of climatic suitability for Plasmodium falciparum transmission based on a temperature-dependent extrinsic incubation period (EIP) and rainfall thresholds for mosquito breeding. Spatial analysis showed that regions with mean temperatures >18 degrees C and monthly rainfall >= 80 mm, predominantly in Central, West, and parts of East Africa, have the highest and most persistent transmission suitability. A significant long-term warming trend (+0.14 degrees C decade-1) links with a shortening of the EIP, enhancing transmission potential. Time-series validation shows that the KMI and its component variables have significant positive correlations with past malaria mortality rates, confirming its advantage as a predictive tool. The findings highlight the necessity of incorporating climate forecasts into public health planning to build effective and robust malaria control strategies under global warming.
The El Ni & ntilde;o-Southern Oscillation (ENSO) will remain the major mode of natural climate variability in a warmer climate. However, how its impacts on crop yields may change under future climate change remains uncertain. Here, we present projected ENSO impacts on yields of maize, wheat, rice and soybean in the middle (2035-2064) and end (2065-2094) of the 21st century under low (Shared Socioeconomic Pathway [SSP] 1-2.6) and high (SSP5-8.5) warming scenarios. The recent climate-crop model ensemble we used (consisting of 12 global gridded crop models [GGCMs], provided by the Agricultural Model Intercomparison and Improvement Project [AgMIP]'s Global Gridded Crop Model Intercomparison [GGCMI] and the Intersectoral Impact Model Intercomparison [ISIMIP] project phase 3) successfully captured the observed major characteristics in the geographical spatial patterns of ENSO-induced yield changes, especially for wheat in La Ni & ntilde;a years and for maize and soybean in both El Ni & ntilde;o and La Ni & ntilde;a years. However, its ability was low for rice. The relatively localized distribution of rice cultivation may limit the model ensemble's ability to capture ENSO-driven yield variability. Overall, the results indicate that ENSO will continue to be a major driver of global yield variability. The negative impacts of El Ni & ntilde;o on maize in eastern Brazil and the positive impacts in Argentina are projected to persist across all warming levels, whereas the negative impact on soybean yields in India may vary depending on the warming level. Yet, substantial uncertainties remain in the projected ENSO impacts, emphasizing the need to improve both climate and crop models to better prepare cropping systems for ENSO-related risks in a warmer climate.
Soil erosion is a critical environmental and agricultural problem in northern Thailand, and rainfall erosivity is a key driver. This paper evaluates projected rainfall erosivity in the Upper Nan Watershed, northern Thailand, using 10 general circulation models (GCMs) from the Coupled Model Intercomparison Project Phase 6 (CMIP6) across 3 Shared Socioeconomic Pathways (SSP; SSP1-2.6, SSP2-4.5, and SSP5-8.5) for the 2030s, 2050s, 2070s, and 2090s. The quantile mapping technique was used to transform CMIP6 output data into daily meteorological data required for rainfall erosivity studies. Monthly rainfall erosivity was computed as the sum of the rainfall erosion index (EI30) of all storms that took place during a given month in the study area. This study used the relationship between monthly precipitation and rainfall erosivity to predict monthly rainfall erosivity under climate change. The results show that rainfall erosivity will increase in all 4 time periods, in each of the SSP scenarios from CMIP6. For the ensemble means of basinwide rainfall erosivity, the increase is expected to range from 3.9-26.5% compared to the baseline period (1986-2014). Long-term multi-model ensemble projections indicate that erosivity is expected to increase by 3.9-5.4% in the 2030s, 7.1-8.8% in the 2050s, 8.0-15.0% in the 2070s, and 9.9-26.5% in the 2090s. However, future rainfall erosivity estimations associated with different GCMs carry some uncertainty. Both monthly and annual peaks indicate important periods of potential erosion in the study area and are associated with increased precipitation induced by climate change, contributing to the increasing potential for rainfall-induced erosion in the study area.
El Ni & ntilde;o diversity is often depicted by the Central Pacific (CP) and Eastern Pacific (EP) El Ni & ntilde;o types; these 2 distinct types of El Ni & ntilde;o-Southern Oscillation (ENSO) regimes differ in their spatial structure, in their intensity and in the time profile of each event. We applied the global vector autoregression (GVAR) econometric framework to estimate the effects of El Ni & ntilde;o diversity on national macroeconomic variables, including gross domestic product (GDP) and consumer price inflation, and global variables, such as world commodity prices and world oil prices. We used quarterly data for the period since ca. 1960, extending quarterly analysis significantly compared to the existing literature. Although the availability of quarterly economic data is limited for this period, we were able to study a set of 26 countries including ones that are directly affected by the El Ni & ntilde;o cycle and others that are affected via climate teleconnections. ENSO diversity matters to macroeconomic outcomes; we found that CP El Ni & ntilde;o shocks raise CPI inflation and non-oil commodity prices, and generate modest but widespread positive GDP responses across many mid- and high-latitude economies, whereas EP El Ni & ntilde;o shocks have much weaker and less systematic effects on inflation, output and commodity prices. The implication of these results is that much of our existing knowledge on the economic effects of the ENSO cycle needs to be revisited to build a more consistent picture across countries and specific historical time periods.
In order to quantitatively evaluate the climate wellness suitability of cities with excellent climate tourism resources in southern China, this study was conducted to construct an evaluation method based on 4 indexes, namely the human comfort index (ICHB), air quality index (AQI), negative oxygen ions (NOIs), and normalized difference vegetation index (NDVI), using the analytic hierarchy process. Eight climate tourism cities (named 'China Natural Oxygen Bar') were selected to calculate the monthly climate wellness index (CWI) for the period 2020-2023 using the above method. The results showed that the AQI of the 8 cities was excellent, with more than 2000 units cm-3 of annual average levels of NOIs. The monthly levels of NDVI and ICHB ranged from 0.61 to 0.83 and from 38 to 83, respectively. The contributions of these parameters to the CWI followed the order of ICHB (0.47), AQI (0.29), NOIs (0.18), NDVI (0.06). The CWI values differed slightly among the seasons, following the order of spring (0.66), autumn (0.63), summer (0.60), and winter (0.39), which was consistent with the current tourism situation. Furthermore, the optimal CWI for different cities occurred in different seasons: Anyuan County recorded the peak CWI in spring and winter, while Jinggangshan City and Zixi County had the highest values in summer and autumn, respectively. This evaluation method provides support for the tourism sector to rationally develop climate health resources and for tourists to choose climate health destinations.
Flash droughts (FDs) are rapid droughts that occur over a short period caused by severe heat waves and precipitation deficiency. FDs have critically increased impacts on agriculture and natural ecosystems. We used the standardized evaporative stress ratio (SESR) to identify FDs during the growing season in a historical simulation and under 4 future shared socio-economic pathway scenarios (SSP1-2.6, SSP2-4.5, SSP3-7.0, SSP5-8.5) of the CMIP6 model based on actual and potential evapotranspiration. Arid regions were classified based on the aridity index (AI), and the frequency, duration, and intensity of FDs were compared and analyzed for different arid regions under different future scenarios. The results showed significant regional differences in global FD events, where the frequency was 10~30%, and the duration was primarily 30~35 d in most areas. FD intensity was mainly moderate, and severe FDs and ranges of FDs under the 4 future scenarios were similar. In addition, the frequency and duration of FDs were slightly higher in humid regions under different scenarios. The proportion of different FD intensities in humid regions was much higher than in other arid regions. Finally, FD regions were divided into Governance, Concern, and Prevention Areas based on the overlayed FD characteristics under the 4 future scenarios, contributing to regionally varied approaches and solutions for the response and prevention of drought disasters.
Spain is the world's leading producer of black truffle Tuber melanosporum Vittad. because of a distinct combination of edaphic properties and climatic conditions in the growing season. Relatively cold winters and moderate summer precipitation are key for a productive harvest; however, climate projections foresee a spatially diverse warming, favoring extreme temperatures and drought conditions which will affect the future distribution of the species. We used a combination of 3 machine-learning-based species distribution modeling methods-maxent, random forest, and boosted regression trees-to estimate the current and future habitat potentiality in mainland Spain, based on historical observations and a collection of environmental variables previously demonstrated as critical parameters for the modeling of black truffle presence. Results showed a notable change in the distribution under 2 future scenarios, with a total loss of between 22 and 32% of the current habitat, especially in zones with hot temperatures and low precipitation. This was partially compensated with the colonization of new areas (+25%) in colder and more humid climates. Overall, the greatest changes in future distribution scenarios were associated with maximum summer temperatures, summer precipitation, elevation, and edaphic pH. While significant differences were found in the contribution of the predictors to the 3 models, the spatial distribution results were similar. By illustrating the climate-driven redistribution of a high-value crop, our findings underscore the importance of integrating ecological modeling into agricultural policy and land-use planning to ensure long-term resilience of rural economies.
Faced with the growing climate crisis, there are calls for transformative adaptation, a climate response that addresses the root causes of vulnerability and requires radical change in food systems. Transformation of food systems is hence essential to deal with the ongoing climate crisis and to fulfil wider sustainability and development challenges from local to global scales. Transformed food systems must minimise vulnerability to shocks while delivering environmental, social and economic benefits. This need can only be met by embracing food system transformation as a process of social and individual change that contributes to transformative adaptation. The scale of this societal and environmental challenge demands an equally grand vision-one that integrates climate, sustainability and systems thinking with human psychology, beliefs and values, and shared understandings. Such a vision, with community and individual transformation at its core, has the potential to deliver multiple benefits to society through transformative food system change, livelihood resilience and climate change adaptation and mitigation. Fostering conditions that support both individual transformation and the development of collective understanding therefore needs to be a core part of any strategies to transform food systems. In this opinion piece, we provide a framework to guide this transformative drive towards societal climate resilience.
Tribal populations, in an Indian setting, primarily reside in regions sensitive to climate change and maintain close relationships with their surrounding environment. Climate change drastically alters or destabilises these regions or the ecosystems contained within them, and puts the lives of tribal groups at risk. This paper attempts to improve understanding of vulnerability to climate change, and the resulting impacts, from tribal groups' perspectives, which are captured by the 3 components of vulnerability: exposure, sensitivity, and adaptive capacity. A cross-sectional survey was carried out among the Gonds, a tribal population in Madhya Pradesh. The findings indicated that most of the Gonds acknowledge the changes occurring in the climate system and its varied impacts. Their perceptions indicated that they have high levels of exposure and sensitivity and low adaptive capacity towards climate change and its impacts, with overall vulnerability falling into the 'high' category. Among all 3 components of vulnerability, sensitivity received the highest mean score, followed by exposure and adaptive capacity. The environmental changes that were documented are of concern to the tribal communities, as they directly affect their sustenance. Understanding vulnerability from their perspective is critical in building climate-resilient tribal communities, as it facilitates the targeting of factors that contribute to their vulnerability, and identifies obstacles that hinder achieving optimum levels of adaptive capacity.
Tropical cyclones (TCs), the most destructive natural disasters of this century, have significantly impacted climate change in humid subtropical China. Utilizing high-resolution climate proxy indicators to study the historical activity of TCs can enhance our understanding of climate changes caused by TCs and aid in risk management. Intra-annual density fluctuations (IADFs), reflecting short-term climate events in tree rings, can capture extreme climate data related to the variability of TCs. In this study, we established a chronology of the frequency of latewood intra-annual density fluctuations (L-IADFs) of Pinus massoniana Lamb. in humid subtropical China. The frequency of L-IADFs was positively related to the sea surface temperature in the regions surrounding the South China Sea and the western Pacific from July to September, August precipitation and the Standardized Precipitation Evapotranspiration Index in 1956-2015. By combining the daily growth rates simulated by the Vaganov-Shahikin model with the measured positions of L-IADFs (54-71%) in the tree rings, it was calculated that L-IADFs occurred mainly from June to September. Linear regression of TC precipitation/total precipitation from July to September explained approximately 30% of the variance in the stable frequency of L-IADFs. Consequently, IADFs can serve as an effective climate proxy to study the impact of TC precipitation in humid subtropical China. Future research can further expand the sample area and tree species, using L-IADFs to reconstruct the history of TCs over a longer timescale, providing a scientific basis for predicting and responding to climate change.
Holocene climate reconstructions are characterized by rather smooth temperature trends owing to the low temporal resolution, dating uncertainty, and non-climatic noise of underlying proxy records. In this study, we apply methods to refine such reconstructions in Europe by calibrating a network of 126 low-resolution Holocene proxies against a continental-scale (smoothed) high-resolution temperature reconstruction over the past 2000 yr. This approach differentiates between 35 records that correlate with the shorter continental reconstruction, and 91 records that do not correlate or have other issues such as abrupt variance, temperature level, or temporal resolution changes. The separation into 'calibrating' and 'non-calibrating' proxies has limited statistical skill, however. This is because the smoothed record autocorrelations exceed r = 0.99 and constrain the degrees of freedom of any correlation-based analysis. The selection based on fit with a common target naturally increases covariance among the records, from an inter-series correlation (Rbar) of 0.00 for all 126 proxies, to Rbar = 0.33 for the 35 calibrating proxies over the Common Era (CE). Covariance in the latter group also reached Rbar = 0.30 during the period Before the Common Era (BCE), compared to Rbar = 0.04 among the non-calibrating proxies. This increase in covariance prior to the CE, while not statistically significant, indicates that the selection process based on fit with a much shorter, 2000 yr target may have improved the temperature reconstruction over the Holocene.
Influenza incidence has been proven to be closely associated with low temperatures. However, the role of temperature fluctuations in affecting influenza incidence is less quantified, especially in the context of global warming. This study constructed a short-term weather variability index (SWVI) to measure the cumulative fluctuations of minimum temperature between 2 consecutive weeks and comprehensively analyzed the impact of SWVI on current and future risks of influenza incidence in the mid-latitude region of Hubei Province, China. Influenza-like illnesses (ILI) data (2009-2020), meteorological observation data from 76 national stations (1979-2020), 10 global climate models in the Coupled Model Intercomparison Project Phase 6 (CMIP6) and distributed lag nonlinear modeling were used for analysis. The results showed that (1) the intra-annual variation of SWVI and the percentage of ILI (ILI%) in Hubei exhibited similar bimodal structures. A more significant correlation was detected between SWVI and ILI% than with sustained low temperatures, suggesting that SWVI may serve as a stimulus for influenza incidence. (2) From winter to spring, the response of relative risk (RR) of ILI% to SWVI was faster and greater than to low temperature. The most significant impact of SWVI on influenza incidence was observed at a short-term lag of 0 wk, with the highest increase in RR of ILI% recorded at 84.1% (95% confidence interval [CI]: 25.7-169.6%) for each 1 degrees C increase in SWVI. In age-stratified analysis, younger people were more susceptible to weather variability than the elderly. (3) In the past 40 yr, the annual SWVI increased consistently, particularly in central and eastern Hubei. CMIP6 projections showed that SWVI would continue to increase with a stronger amplitude under scenarios of higher emissions. The risk of influenza incidence caused by short-term weather variability will inevitably increase, with the greatest risk anticipated at the end of the 21st century (76.4%).
Global surface temperature is an important indicator of climate change. Its long-term shifts reflect changes in the balance between absorbed global mean solar radiation and emitted thermal infrared radiation to space, plus natural climatic variability. Earth's energy imbalance and the responses and feedbacks from many different and interacting Earth systems are all affecting global temperature, but the rhythms and sequence of events behind the ongoing trajectory of this key indicator remain elusive. Here we provide novel analyses of global surface temperature anomalies (GSTA) over the period 1880-2022 by means of a framework based on indicators of critical transition (multiscale variability, variance, autocorrelation and trend). Our analyses indicate that a tipping point was already crossed between 1965 and 1977 and that the climatic system has moved to a phase of critical transition. We identify a sequence of events that punctuate the transition and reveal that changes in global temperature are now severely accelerating. Theory indicates that, in the absence of large reductions in greenhouse gas emissions, the current global warming trend is expected to continue to escalate until an inflection point is eventually reached. These results are important because they suggest that regional consequences of global climate change on the different natural and human systems of our planet, including biodiversity, human health, socio-economic and geopolitical systems, may rapidly intensify and reach levels unseen in recent human history.
To evaluate the risk of disease spread by vectors, future mosquito population dynamics, which are strongly affected by climate change, must be predicted. Despite the importance of precipitation to mosquito development, predictions of mosquito population dynamics have rarely considered its effects. The most effective approach to predict future precipitation data is to use general circulation model outputs with downscaling. However, differences caused by various downscaling methods or models make it difficult to accurately evaluate future biological population dynamics. This study considered the uncertainty in future daily precipitation by downscaling the data into 4 patterns, and predicted the population dynamics of 2 mosquito species for each pattern in Tokyo, Japan. Population dynamics were simulated using baseline meteorological data, and sensitivity analysis was conducted to assess the effects of precipitation on the population dynamics of each species. Future population dynamics under Shared Socioeconomic Pathway (SSP) 1-2.6 and SSP5-8.5, each with 4 different precipitation patterns, were then estimated. Culex pipiens abundance decreased with increasing precipitation frequency, whereas Aedes albopictus abundance showed no clear relationship with the precipitation patterns. In the future, C. pipiens and A. albopictus populations are predicted to decrease and increase, respectively, with differences in the rate of change driven by precipitation patterns. This study demonstrates the importance of considering the uncertainty in future precipitation when evaluating future mosquito population dynamics.
Global preharvest crop forecasting is a field of research that has emerged since the 2010s. Such research is expected to contribute to making globally interconnected agri-food systems more climate resilient. In November 2024, the global preharvest crop forecasting system entered in its operational phase at the Asia-Pacific Economic Cooperation (APEC) Climate Center (APCC). Using the APCC multi-model ensemble (MME) temperature and precipitation forecasts as main inputs, the system provides monthly information on climate-induced changes in the yields of maize, rice, wheat, and soybean in the ongoing season, 6 to 3 mo before the harvest. This information is a key outcome of the research project jointly conducted by the National Agriculture and Food Research Organization (NARO), Japan, and APCC, Republic of Korea, for the period 2017-2023. To commemorate this milestone, this Opinion Piece briefly summarizes the global crop forecasting research done by our team and possible avenues for future research to make such forecasts more reliable and widely adopted by societies.
Assessing the impact of sea level changes on airports across the present century is a pressing issue for the rapidly expanding aviation sector and, more generally, for establishing adaptation strategies. To date, these assessments have assumed that future melting of ice sheets and glaciers leads to globally uniform sea level changes. We summarize recent geophysical research that highlights the extreme geographic variability in sea level that will occur in response to such melting—a variability captured in so-called sea level fingerprints. As a case study, we present modeling predictions of sea level change to 2100 CE based on a suite of published projections of polar ice mass flux and consider the implications of these results for airports identified as being at particularly high risk from sea level rise. We conclude that this important source of sea level variability should be incorporated—together with other processes that imprint a geographic pattern on sea level (e.g. storm surges, tides, thermosteric and ocean dynamic changes)—into projections of airport risks in a warming world.
In recent decades, disaster, catastrophe, and climate change education have gained global importance, emphasizing the need for effective curriculum integration to raise awareness and foster mitigation efforts. This review highlights the incorporation of this exceptional education into Pakistan’s curricula, drawing on international research. Relevant studies published from 2015 to 2024 were systematically reviewed through Google Scholar and Science Direct using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines with 4 stages: identification, screening, eligibility, and inclusion. Nineteen articles revolved around curriculum development, revealing diverse approaches such as embedding disaster and climate change issues within curricula or creating dedicated adaptation and mitigation programs. The findings emphasize significant variations in educational strategies and underscore the need for research to develop a more comprehensive and sustainable disaster risk reduction approach for Pakistan’s education system and state institutions. This approach would enhance preparedness and resilience to climate change impacts in the region.
