Probabilistic evaluation of the impact of compound dry-hot events on global maize yields

Intra-growing season dry-wet spell pattern is a pivotal driver of maize yield variability in sub-Saharan Africa

Climate variability plays a crucial role in the annual fluctuations of crop yields, posing a substantial threat to food security. Maize, the main cereal in sub-Saharan Africa, has shown varied yield trends during increasingly warmer growing seasons. Here we explore how sub-seasonal dry-wet spell patterns contribute to this variability, considering the spatial heterogeneity of crop responses, to map weather-related risks at a regional level. Our results show that shifts in specific dry-wet spell patterns across growth stages influence maize yield fluctuations in sub-Saharan Africa, explaining up to 50-60% of the interannual variation, which doubles that explained by mean changes in precipitation and temperature (30-35%). Precipitation primarily drives the onset of dry spells, while the influence of temperature increases with event intensity and peaks at the start of the growing season. Our large-scale, data-limited analysis approach has the potential to inform climate-smart agriculture in developing regions.

The impact of compound drought and heatwave events from 1982 to 2022 on the phenology of Central Asian grasslands

In the context of global warming, the occurrence and severity of extreme events like atmospheric drought (AD) and warm spell duration index (WSDI) have increased, causing significant impacts on terrestrial ecosystems in Central Asia's arid regions. Previous research has focused on single extreme events such as AD and WSDI, but the effect of compound hot and dry events (CHWE) on grassland phenology in the arid regions of Central Asia remains unclear. This study utilized structural equation modeling (SEM) and the Pettitt breakpoint test to quantify the direct and indirect responses of grassland phenology (start of season - SOS, length of season - LOS, and end of season - EOS) to AD, WSDI, and CHWE. Furthermore, this research investigated the threshold of grassland phenology response to compound hot and dry events. The research findings indicate a significant increasing trend in AD, WSDI, and CHWE in the arid regions of Central Asia from 1982 to 2022 (0.51 day/year, P < 0.01; 0.25 day/year, P < 0.01; 0.26 day/year, P < 0.01). SOS in the arid regions of Central Asia showed a significant advancement trend, while EOS exhibited a significant advance. LOS demonstrated an increasing trend (-0.23 day/year, P < 0.01; -0.12 day/year, P < 0.01; 0.56 day/year). The temperature primarily governs the variation in SOS. While higher temperatures promote an earlier SOS, they also offset the delaying effect of CHWE on SOS. AD, temperature, and CHWE have negative impacts on EOS, whereas WSDI has a positive effect on EOS. AD exhibits the strongest negative effect on EOS, with an increase in AD leading to an earlier EOS. Temperature and WSDI are positively correlated with LOS, indicating that higher temperatures and increased WSDI contribute to a longer LOS. The threshold values for the response of SOS, EOS, and LOS to CHWE are 16.14, 18.49, and 16.61 days, respectively. When CHWE exceeds these critical thresholds, there are significant changes in the response of SOS, EOS, and LOS to CHWE. These findings deepen our understanding of the mechanisms by which extreme climate events influence grassland phenology dynamics in Central Asia. They can contribute to better protection and management of grassland ecosystems and help in addressing the impacts of global warming and climate change in practice.

Weather Extremes Shock Maize Production: Current Approaches and Future Research Directions in Africa

Extreme weather events have led to widespread yield losses and significant global economic damage in recent decades. African agriculture is particularly vulnerable due to its harsh environments and limited adaptation capacity. This systematic review analyzes 96 articles from Web of Science, Science Direct, and Google Scholar, focusing on biophysical studies related to maize in Africa and worldwide. We investigated the observed and projected extreme weather events in Africa, their impacts on maize production, and the approaches used to assess these effects. Our analysis reveals that drought, heatwaves, and floods are major threats to African maize production, impacting yields, suitable cultivation areas, and farmers' livelihoods. While studies have employed various methods, including field experiments, statistical models, and process-based modeling, African research is often limited by data gaps and technological constraints. We identify three main gaps: (i) lack of reliable long-term experimental and empirical data, (ii) limited access to advanced climate change adaptation technologies, and (iii) insufficient knowledge about specific extreme weather patterns and their interactions with management regimes. This review highlights the urgent need for targeted research in Africa to improve understanding of extreme weather impacts and formulate effective adaptation strategies. We advocate for focused research on data collection, technology transfer, and integration of local knowledge with new technologies to bolster maize resilience and food security in Africa.

Assessment of the combined vulnerability to droughts and heatwaves in Shandong Province in summer from 2000 to 2018

Droughts and heat waves exhibit synergistic effects and are among the world's most costly disasters. To explore the spatiotemporal differences and formation mechanisms of the combined vulnerability to droughts and heat waves in Shandong Province over the past 20 years, a vulnerability scoping diagram (VSD) model with three dimensions-exposure, sensitivity, and adaptability-was constructed to assess and compare the combined vulnerability to high-temperature and drought events, considering economic and social conditions. The results showed that (1) over the past 20 years, heat waves and droughts have increased in Shandong Province. The number of high-temperature events significantly increased in the west and decreased along the eastern coast, and drought change was characterized by an increase in the south and a decrease in the north. (2) The combined exposure to summer droughts and heat waves in Shandong Province showed a significant increasing trend (P < 0.05) at a rate of approximately 0.072/10a; the combined sensitivity significantly decreased (P < 0.05) at a rate of approximately 0.137/10a, and the combined adaptability continued to increase at a rate of approximately 0.481/10a. (3) The combined vulnerability to summer droughts and heat waves in the western inland area of Shandong Province was high and gradually decreased toward the southeastern coast. The overall decrease trend was nonsignificant with a decrease of approximately 0.126/10a, and the decline rate decreased from northwest to southeast, in which Laiwu, Yantai, Jinan, and Zibo cities exhibited a significant decreasing trend (P < 0.05). Although the compound vulnerability of Shandong Province has decreased insignificantly, the frequency of combined drought and heat wave events has increased, and the combined vulnerability will increase in the future.

Understanding drought propagation through coupling spatiotemporal features using vine copulas: A compound drought perspective

Accurately evaluating drought impact on agriculture poses a challenge to regional food security, particularly in compound drought (i.e., meteorological and agricultural drought co-occurring) scenarios. This study presents a novel approach utilizing Vine copula for coupling spatiotemporal features to evaluate drought propagation. Three-dimensional clustering method was employed to identify meteorological and agricultural drought events, which excelled in capturing dynamic evolution characteristics (duration, area, severity, etc.) as well as integrating them into comprehensive meteorological drought intensity (IMD) and agricultural drought intensity (IAD). Through spatiotemporal matching, compound drought events were extracted from the meteorological-agricultural drought event pairs. From compound drought perspective, compound duration (CD) and compound area (CA) were devised to characterize drought propagation potential across time and space. Finally, the Vine copula method was employed to model the interdependence between four key coupling features, namely IMD, IAD, CD, and CA, and evaluate the probability of triggering agricultural drought with different intensity levels. Results showed that CD and CA can respectively characterize the temporal and spatial accumulation scale of drought propagation. At a certain IMD level, CD significantly influences the propagation probability (i.e., "stratification" phenomenon), while CA increases the probability proportionally. Probability evaluation lacking spatiotemporal information may underestimate the likelihood of drought propagation characterized by "low-IMD" but "long-CD" or "large-CA". The four-dimensional Vine copula structure can effectively couple dependence relationships of compound drought characteristics, and exhibits reliable robustness. This research provides stakeholders accurate probabilistic evaluation under compound drought scenarios, offering new insight into drought propagation.

A generalized procedure for joint monitoring and probabilistic quantification of extreme climate events at regional level

Droughts and heat waves are currently recognized as two of the most serious threats associated with climate changes. Drought is characterized by prolonged dry periods, low precipitation, and high temperature, while heat wave refers to an extended period of exceptionally high temperature, surpassing the region's average for that time of year. There is a close relationship between droughts and heat waves, as both are often caused by similar weather patterns and can exacerbate each other's impacts. Therefore, it is crucial to monitor and quantify both droughts and heat waves jointly at a regional level in order to develop sustainable policies and effectively manage water resources. This article develops a new index, the standardized composite index for climate extremes (SCICE), for joint monitoring and probabilistic quantification of extreme climate events at regional level. The procedure of SCICE is mainly based on the joint standardization of standardized precipitation index (SPI) and standardized temperature index (STI). In the application of SCICE, results reveal that the long-term probabilities of the joint occurrence of dry and hot events are significantly greater than those of wet and cold events. Furthermore, the outcomes of the comparative assessment support the validity of using SCICE as a compact statistical approach in regional drought analysis. In summation, the study demonstrates the capability of SCICE to effectively characterize and assess the joint monitoring of drought and heat waves at a regional level, providing a comprehensive approach to understanding the joint impact of climate extremes.

Consideration of compound drivers and impacts in the disaster risk reduction cycle

Consideration of compound drivers and impacts are often missing from applications within the Disaster Risk Reduction (DRR) cycle, leading to poorer understanding of risk and benefits of actions. The need to include compound considerations is known, but lack of guidance is prohibiting practitioners from including these considerations. This article makes a step toward practitioner guidance by providing examples where consideration of compound drivers, hazards, and impacts may affect different application domains within disaster risk management. We discern five DRR categories and provide illustrative examples of studies that highlight the role of "compound thinking" in early warning, emergency response, infrastructure management, long-term planning, and capacity building. We conclude with a number of common elements that may contribute to the development of practical guidelines to develop appropriate applications for risk management.

Increased probability of hot and dry weather extremes during the growing season threatens global crop yields

Although extreme weather events recur periodically everywhere, the impacts of their simultaneous occurrence on crop yields are globally unknown. In this study, we estimate the impacts of combined hot and dry extremes as well as cold and wet extremes on maize, rice, soybean, and wheat yields using gridded weather data and reported crop yield data at the global scale for 1980-2009. Our results show that co-occurring extremely hot and dry events have globally consistent negative effects on the yields of all inspected crop types. Extremely cold and wet conditions were observed to reduce crop yields globally too, although to a lesser extent and the impacts being more uncertain and inconsistent. Critically, we found that over the study period, the probability of co-occurring extreme hot and dry events during the growing season increased across all inspected crop types; wheat showing the largest, up to a six-fold, increase. Hence, our study highlights the potentially detrimental impacts that increasing climate variability can have on global food production.

Risk of Crop Yield Reduction in China under 1.5 °C and 2 °C Global Warming from CMIP6 Models

Warmer temperatures significantly influence crop yields, which are a critical determinant of food supply and human well-being. In this study, a probabilistic approach based on bivariate copula models was used to investigate the dependence (described by joint distribution) between crop yield and growing season temperature (TGS) in the major producing provinces of China for three staple crops (i.e., rice, wheat, and maize). Based on the outputs of 12 models from the Coupled Model Intercomparison Project Phase 6 (CMIP6) under Shared Socioeconomic Pathway 5-8.5, the probability of yield reduction under 1.5 °C and 2 °C global warming was estimated, which has great implications for agricultural risk management. Results showed that yield response to TGS varied with crop and region, with the most vulnerable being rice in Sichuan, wheat in Sichuan and Gansu, and maize in Shandong, Liaoning, Jilin, Nei Mongol, Shanxi, and Hebei. Among the selected five copulas, Archimedean/elliptical copulas were more suitable to describe the joint distribution between TGS and yield in most rice-/maize-producing provinces. The probability of yield reduction was greater in vulnerable provinces than in non-vulnerable provinces, with maize facing a higher risk of warming-driven yield loss than rice and wheat. Compared to the 1.5 °C global warming, an additional 0.5 °C warming would increase the yield loss risk in vulnerable provinces by 2-17%, 1-16%, and 3-17% for rice, wheat, and maize, respectively. The copula-based model proved to be an effective tool to provide probabilistic estimates of yield reduction due to warming and can be applied to other crops and regions. The results of this study demonstrated the importance of keeping global warming within 1.5 °C to mitigate the yield loss risk and optimize agricultural decision-making in vulnerable regions.

Future global concurrent droughts and their effects on maize yield

Droughts are one of the most devastating natural disasters. Droughts can co-exist in different forms (e.g. meteorological, hydrological, and agricultural) as concurrent droughts. Such concurrent droughts can have far reaching implications for crop yield and global food security. The present study aims to assess global concurrent drought traits and their effects on maize yield under climate change. The standardized indices of precipitation, runoff, and soil moisture incorporated as multivariate standardized drought index (MSDI) using copula functions are used to quantify the concurrent droughts. The ensemble data of several General Circulation Models (GCMs) considering the high emission scenario of Coupled Model Intercomparison Project phase 6 (CMIP6) are utilized. Applying run theory on a time series (1950-2100) of MSDI values, the duration, severity, areal coverage, and average areal intensity of concurrent droughts are computed. The temporal evolution of drought duration and severity are compared among historical (1950-2014), near future (2021-2060), and far future (2061-2100) timeframes. The results indicate that the most vulnerable regions in the late 21st century are Central America, the Mediterranean, Southern Africa, and the Amazon basin. The indices and spatial extent of the individual droughts are used as predictor variables to predict the country-level crop index of the top seven producers of maize. The historical dynamics between maize yield and different drought forms are projected using XGBoost (Extreme Gradient Boosting) algorithms. The future temporal changes in drought-crop yield dynamics are tracked using probabilities of various drought forms under yield-loss conditions. The conditional concurrent drought probabilities are as high as 84 %, 64 %, and 37 % in France, Mexico, and Brazil, revealing that concurrent drought affects the maize yield tremendously in the far future. This approach of applying statistical and soft-computing techniques could aid in drought mitigation under changing climatic conditions.

U.S. winter wheat yield loss attributed to compound hot-dry-windy events

Climate extremes cause significant winter wheat yield loss and can cause much greater impacts than single extremes in isolation when multiple extremes occur simultaneously. Here we show that compound hot-dry-windy events (HDW) significantly increased in the U.S. Great Plains from 1982 to 2020. These HDW events were the most impactful drivers for wheat yield loss, accounting for a 4% yield reduction per 10 h of HDW during heading to maturity. Current HDW trends are associated with yield reduction rates of up to 0.09 t ha-1 per decade and HDW variations are atmospheric-bridged with the Pacific Decadal Oscillation. We quantify the "yield shock", which is spatially distributed, with the losses in severely HDW-affected areas, presumably the same areas affected by the Dust Bowl of the 1930s. Our findings indicate that compound HDW, which traditional risk assessments overlooked, have significant implications for the U.S. winter wheat production and beyond.

Preliminary Expression Analysis of the OSCA Gene Family in Maize and Their Involvement in Temperature Stress

Hyperosmolality-gated calcium-permeable channels (OSCA) are characterized as an osmosensor in plants; they are able to recognize and respond to exogenous and endogenous osmotic changes, and play a vital role in plant growth and adaptability to environmental stress. To explore the potential biological functions of OSCAs in maize, we performed a bioinformatics and expression analysis of the ZmOSCA gene family. Using bioinformatics methods, we identified twelve OSCA genes from the genome database of maize. According to their sequence composition and phylogenetic relationship, the maize OSCA family was classified into four groups (Ⅰ, Ⅱ, Ⅲ, and Ⅳ). Multiple sequence alignment analysis revealed a conserved DUF221 domain in these members. We modeled the calcium binding sites of four OSCA families using the autodocking technique. The expression profiles of ZmOSCA genes were analyzed in different tissues and under diverse abiotic stresses such as drought, salt, high temperature, and chilling using quantitative real-time PCR (qRT-PCR). We found that the expression of twelve ZmOSCA genes is variant in different tissues of maize. Furthermore, abiotic stresses such as drought, salt, high temperature, and chilling differentially induced the expression of twelve ZmOSCA genes. We chose ZmOSCA2.2 and ZmOSCA2.3, which responded most strongly to temperature stress, for prediction of protein interactions. We modeled the calcium binding sites of four OSCA families using autodocking tools, obtaining a number of new results. These results are helpful in understanding the function of the plant OSCA gene family for study of the molecular mechanism of plant osmotic stress and response, as well as exploration of the interaction between osmotic stress, high-temperature stress, and low-temperature stress signal transduction mechanisms. As such, they can provide a theoretical basis for crop breeding.

Hotspots, co-occurrence, and shifts of compound and cascading extreme climate events in Eurasian drylands

Eurasian drylands are the regions that are most vulnerable to climate change. Climate extremes have caused enormous or even devastating impacts on ecosystems and the social economy in this region, and the compound climate extremes (com_CEs, two or more extreme events occurring simultaneously) and cascading climate extremes (cas_CEs, two or more extreme events occurring successively) have exacerbated these problems. However, little is known about the occurrence patterns of com_CEs and cas_CEs in the Eurasian drylands. Based on the ERA5 reanalysis data range from 1979 to 2020, we improved the methodology for the extraction of co-occurrence events and identified high-frequency types, their hotspots, and occurrence rhythms (seasonally and annually) in Eurasian drylands. Our results showed that com_CEs and cas_CEs have high similarities in the types and spatial hotspots of extreme events; however, the former has a wider geographical and spatial distribution, and the latter has a longer duration. Specifically, co-occurring drought and heatwave events (DH) frequently appear in South Asia and western mid-latitude regions during summer, while in the winter, high latitude regions should be alert to the co-occurrence of drought and low-temperature events (DT). Central Asia and the Mongolian Plateau regions are prone to frequent drought and wind events (DW), and wind and high precipitation events (WP) in the spring and autumn. We have noticed that mid-latitude may suffer from extreme events that have never occurred before, such as com_DH being scattered sporadically in the first two decades and suddenly surging in West Asia and East Asia after the year 2000, and com_DT migrating from high-latitude areas such as the Arctic Ocean coast to mid-latitudes. Our results contribute to understanding hotspots of co-occurring CEs in Eurasian drylands, where more efforts will be needed in the future, especially in mid-latitudes which may suffer extreme climate events that have never occurred before.

Crop Yield Loss Risk Is Modulated by Anthropogenic Factors

High crop yield variation between years-caused by extreme shocks on the food production system such as extreme weather-can have substantial effects on food production. This in turn introduces vulnerabilities into the global food system. To mitigate the effects of these shocks, there is a clear need to understand how different adaptive capacity measures link to crop yield variability. While existing literature provides many local-scale studies on this linkage, no comprehensive global assessment yet exists. We assessed reported crop yield variation for wheat, maize, soybean, and rice for the time period 1981-2009 by measuring both yield loss risk (variation in negative yield anomalies considering all years) and changes in yields during "dry" shock and "hot" shock years. We used the machine learning algorithm XGBoost to assess the explanatory power of selected gridded indicators of anthropogenic factors globally (i.e., adaptive capacity measures such as the human development index, irrigation infrastructure, and fertilizer use) on yield variation at a 0.5° resolution within climatically similar regions (to rule out the role of average climate conditions). We found that the anthropogenic factors explained 40%-60% of yield loss risk variation across the whole time period, whereas the factors provided noticeably lower (5%-20%) explanatory power during shock years. On a continental scale, especially in Europe and Africa, the factors explained a high proportion of the yield loss risk variation (up to around 80%). Assessing crop production vulnerabilities on global scale provides supporting knowledge to target specific adaptation measures, thus contributing to global food security.

Compound events and associated impacts in China

Owing to amplified impacts on human society and ecosystems, compound events (or extremes) have attracted ample attention in recent decades. China is particularly vulnerable to compound events due to the fast warming rate, dense populations, and fragile ecological environment. Recent studies have demonstrated tangible effects of climate change on compound events with mounting impacts on the economy, agriculture, public health, and infrastructure in China, posing unprecedented threats that are increasingly difficult to manage. Here, I synthesize recent progress in studies of compound events and associated impacts in China. Several lines of evidence indicate an increase in the frequency and intensity of multiple types of compound events across China. Future directions in studying compound events in China are suggested, including investigating extremes from a compound perspective, modeling compound events in the Anthropocene, quantitative evaluations of risks, and holistic adaptation measures of compound events.

Increased probability and severity of compound dry and hot growing seasons over world's major croplands

Dry and hot extremes are major sources of risk to crop yields, and their impacts are expected to increase under future global warming. The co-occurring dry and hot conditions during crop growing seasons have amplified impacts on crop health that are even larger than the sum of their individual impacts, which may cause crop failure. In this study, we focus on the compound dry and hot growing seasons (hereafter CDHGS) for global wheat, rice, maize and soybean in the period 1951-2020. Total precipitation (TP) and accumulated active temperature (AAT) are used as indicators of overall water stress and heat stress, respectively, at the growing season scale. A copula model is used to construct joint distributions of TP and AAT sequences to investigate the joint behavior of dry and hot conditions during crop growing seasons. Our results indicate that after 1980, the growing seasons of the four crops become drier and more rapidly hotter across the globe, the probability of extreme CDHGS (P(TP ≤ TP₂₅,AAT > AAT₇₅)) increases in more than 80% of global croplands, the severity of CDHGS increases in more than 83% of global croplands, especially in Europe, Central Africa and eastern China. This study provides a global dimension analysis on the changes in compound dry and hot stresses within crops growing seasons in the context of global warming, offering helpful techniques to study the interaction between multi-hazards that occur during crop growth processes, which can effectively contribute to guiding the decision-making processes related to risk reduction and agricultural practices.

The compound effects of drought and high temperature stresses will be the main constraints on maize yield in Northeast China

Compound climate extremes such as drought and high temperature have a greater impact on agricultural production than the individual extremes. An increasing frequency and intensity of the compound climate extremes has been observed and projected under climate change, yet partitioning the total impacts to individual ones on crop yield has not been well assessed. In this study, we assessed the compound and separate effects of drought and high temperature on maize yield under 9 climate-year types (CYTs) with different combinations of precipitation and temperature in Northeast China (NEC). The well-validated Agricultural Production Systems Simulator (APSIM) model was used to simulate the maize yield, driven by historical (1981-2017) and future climate data (2021-2060). The results show that CYTs of warm (warm-dry, warm-wet, warm) are prominent in the future under both Representative Concentration Pathway (RCP) 4.5 and 8.5 scenarios. However, CYT of warm-wet increased mostly (11.5%) under RCP8.5, while warm-dry increased most (12.3%) under RCP4.5. The magnitude of maize yield loss caused by the compound of high temperature and drought (18.75%) is higher than the individual ones (drought 17.32% and high temperature 1.27%). There are variations in the effects of stresses on maize yield among CYTs and the yield reductions by the compound effects of drought and high temperature were warm-dry > warm > rainless > warm-wet > normal > cold-dry > cold > rainy > cold-wet. In addition, the yield loss was negatively correlated with T_max and VPD_max but positively correlated with Prec. These findings imply the importance of fully considering the selection of heat and drought-resistant varieties and implementing supplementary irrigation for future climate mitigation strategies during maize production in NEC.

Effects of Land Cover Changes on Compound Extremes over West Africa Using the Regional Climate Model RegCM4

This study aims to characterize the impacts of the Sahel–Sahara interface reforestation on compound extremes in the Sahel region during the West African monsoon season (June–July–August–September, JJAS). For this purpose, we performed a simulation with the standard version of the RegCM4 model, and another simulation with the altered version of the same model, taking into account the incorporated forest. Results show that reforestation may strongly influence the frequency of individual extreme events (dry and warm days) by decreasing them over and off the reforested zone. The reduction in these extreme dry and warm days may be due partly to the strengthening of the atmospheric moisture content over most parts of the West African domain and the weakening of the sensible heat flux south of 16° N. The analysis also shows an increase in extreme wet days over and off the reforested zone, which could be associated partly with the strengthening of evapotranspiration over most parts of the West African domain, including the reforested area. The analysis of compound extremes shows a strong occurrence of the compound dry/warm mode over the northern Sahel for both runs, probably due to the weak precipitation recorded in this zone. Both experiments also simulated a strong compound wet/warm mode occurrence over the Sahel due to a high rainfall occurrence over this region. When comparing both runs, the impact of the reforestation was to decrease (increase) the compound extreme dry/warm (wet/warm) mode over the reforested zone. The dry/warm mode decrease is consistent with that of individual extreme dry and warm days, while the compound wet/warm mode increase may be driven by that of the extreme wet days. Finally, when considering the seasonal cycle, the dry/warm mode exhibits a more substantial decrease in the beginning (June–July, JJ) than during the peak of the West African summer monsoon season (August–September, AS). Moreover, reforestation similarly affects the compound wet/warm mode in JJ and AS by increasing it in the reforested region and decreasing it over the Southern Sahel (south of 15° N). This work suggests that reforestation may be a good solution for West African policymakers to mitigate climate change over the region and to develop better strategies for water resource management.

Population exposure to compound extreme events in India under different emission and population scenarios

It is well understood that India is largely exposed to different climate extremes including floods, droughts, heat waves, among others. However, the exposure of co-occurrence of these events is still unknown. The present analysis, first study of its kind, provides the projected changeability of five different compound extremes under three different emission scenarios (SSP2-4.5, SSP3-7.0, and SSP5-8.5). These changes are combined with population projection under SSP2, SSP3, and SSP5 scenarios to examine the total exposure in terms of number of persons exposed during 2021-2060 (T1) and 2061-2100 (T2). Here, the outputs from thirteen GCMs are used under CMIP6 experiment. The findings from the study show that all the compound extremes are expected to increase in future under all the emission scenarios being greater in case of SSP5-8.5. The population exposure is highest (2.51- to 4.96-fold as compared to historical) under SSP3-7.0 scenario (2021-2100 i.e., T1 and T2) in case of coincident heat waves and droughts compound extreme. The total exposure in Central Northeast India is projected to be the highest while Hilly Regions are likely to have the lowest exposure in future. The increase in the exposure is mainly contributed from climate change, population growth and their interaction depending on different kinds of compound extremes. The findings would help in devising sustainable policy strategies to climate mitigation and adaptation.

Spatial and Temporal Variation of Droughts in the Mongolian Plateau during 1959–2018 Based on the Gridded Self-Calibrating Palmer Drought Severity Index

Drought monitoring is challenging, but it is required for improving agricultural production, protecting the ecological environment, and reducing economic losses in drought-prone regions such as the Mongolian Plateau (MP). This study is a systematic analysis of the spatiotemporal changes in the characteristics of drought events (drought duration, severity, intensity, frequency, peak, and starting season) at the sub-regional scale between 1959 and 2018 based on the run theory and using the gridded self-calibrating Palmer Drought Severity Index (scPDSI) dataset. Principal component analysis and Varimax rotation and the Mann–Kendall trend and Sen’s slope were used for the sub-regional division and drought trend analysis, respectively. In addition, wavelet analysis was employed to analyze drought periodicity and determine the influence of large-scale climate indices on regional drought variation. The study results indicate clear differences in the spatial patterns of drought characteristics in the MP. The northern part suffered from droughts with longer duration and higher severity, whereas more drought events with shorter duration and less severity occurred in the southern part. Most of the MP experienced a relatively wet trend in 1996–2018 compared to the period of 1959–1995. The frequency of spring drought events showed an increasing trend in 1996–2018, unlike in 1959–1995. Some drought events simultaneously affected two or several sub-regions. The wavelet analysis results indicated that the drought periodicity in the MP was 10–64 months. The Arctic Oscillation (Pacific Decadal Oscillation) was significantly correlated with drought in the southern (northern) part.

Droughts across China: Drought factors, prediction and impacts

Drought is a complicated and costly natural hazard and identification of critical drought factors is critical for modeling and forecasting of droughts and hence development of drought mitigation measures (the Standardized Precipitation-Evapotranspiration Index) in both space and time. Here we quantified relationships between drought and 23 drought factors using remote sensing data during the period of 2002-2016. Based on the Gradient Boosting Algorithm (GBM), we found that precipitation and soil moisture had relatively large contributions to droughts. During the growing season, the relative importance of Normalized Difference Water Index (NDWI-7) for SPEI3, SPEI6, SPEI9, and SPEI12 reached as high as 50%. However, during the non-growing season, the Snow Cover Fraction (SCF) had larger fractional relative importance for short-term droughts in the Inner Mongolia and the Loess Plateau which can reach as high as 10%. We also compared Extremely Randomized Trees (ERT), H2O-based Deep Learning (Model developed by H2O.deep learning in R H2O.DL), and Extreme Learning Machine (ELM) for drought prediction at various time scales, and found that the ERT model had the highest prediction performance with R² > 0.72. Based on the Meta-Gaussian model, we quantified the probability of maize yield reduction in the North China Plain under different compound dry-hot conditions. Due to extreme drought and hot conditions, Shandong Province in North China had the highest probability of >80% of the maize yield reduction; due to the extreme hot conditions, Jiangsu Province in East China had the largest probability of >86% of the maize yield reduction.

Underappreciated plant vulnerabilities to heat waves

With climate change, heat waves are becoming increasingly frequent, intense and broader in spatial extent. However, while the lethal effects of heat waves on humans are well documented, the impacts on flora are less well understood, perhaps except for crops. We summarize recent findings related to heat wave impacts including: sublethal and lethal effects at leaf and plant scales, secondary ecosystem effects, and more complex impacts such as increased heat wave frequency across all seasons, and interactions with other disturbances. We propose generalizable practical trials to quantify the critical bounding conditions of vulnerability to heat waves. Collectively, plant vulnerabilities to heat waves appear to be underappreciated and understudied, particularly with respect to understanding heat wave driven plant die-off and ecosystem tipping points.

Impact of extreme weather conditions on European crop production in 2018

Extreme weather increases the risk of large-scale crop failure. The mechanisms involved are complex and intertwined, hence undermining the identification of simple adaptation levers to help improve the resilience of agricultural production. Based on more than 82 000 yield data reported at the regional level in 17 European countries, we assess how climate affected the yields of nine crop species. Using machine learning models, we analyzed historical yield data since 1901 and then focus on 2018, which has experienced a multiplicity and a diversity of atypical extreme climatic conditions. Machine learning models explain up to 65% of historical yield anomalies. We find that both extremes in temperature and precipitation are associated with negative yield anomalies, but with varying impacts in different parts of Europe. In 2018, Northern and Eastern Europe experienced multiple and simultaneous crop failures-among the highest observed in recent decades. These yield losses were associated with extremely low rainfalls in combination with high temperatures between March and August 2018. However, the higher than usual yields recorded in Southern Europe-caused by favourable spring rainfall conditions-nearly offset the large decrease in Northern European crop production. Our results outline the importance of considering single and compound climate extremes to analyse the causes of yield losses in Europe. We found no clear upward or downward trend in the frequency of extreme yield losses for any of the considered crops between 1990 and 2018. This article is part of the theme issue 'Impacts of the 2018 severe drought and heatwave in Europe: from site to continental scale'.

More frequent and widespread persistent compound drought and heat event observed in China

Compound drought and heat event (CDHE) causes severe impacts on agriculture, ecosystem, and human health. Based on daily maximum surface air temperature and meteorological drought composite index data in China, changing features of CDHEs in warm season from 1961 to 2018 is explored at a daily time scale based on a strict and objective definition in this study. Results reveal that CDHEs have occurred more frequently and widely in China, especially since the late 1990s. Notably, such changes are more obvious in Southwest China, eastern Northwest China, northern North China, and the coastal area of southeastern China. A prominent feature is that persistent CDHEs on a daily scale have increased significantly. To better understand climate change of compound extreme events, further studies on the physical mechanism, especially attribution analyses at a regional scale, are urgently needed.

Quantifying likelihoods of extreme occurrences causing maize yield reduction at the global scale

A variety of weather and climate extremes (e.g., droughts, heatwaves) can lead to negative impacts on crop yields and food security. It is thus important to understand likelihoods of extreme occurrences causing crop yield reduction for enhanced resilience of the food system. Here, we investigate the likelihood of occurrences of dry, hot, and compound dry-hot conditions causing crop yield reduction for ten maize-producing countries based on climate observations and country-level maize yields for the period 1961-2016. The likelihood of occurrences of different extremes causing maize yield reduction is quantified using a multivariate statistical model. Results show that the multivariate model performs well in quantifying the likelihood of extreme occurrences (i.e., dry, hot and compound dry-hot conditions) causing maize yield reduction. Overall, the likelihood of occurrences of the above three conditions leading to yield reduction varies among ten maize-producing countries and that of compound dry-hot condition is the highest for most countries, which is shown to be closely related to the precipitation-temperature dependence of each country. Moreover, the likelihood of compound dry-hot occurrences becomes higher as the severity of crop yield reduction increases. These findings highlight significant impacts of compound dry-hot conditions on maize yield reduction and provide valuable information for formulating effective agricultural measures under global warming.