Historical and Projected Cropland Impacts of Heatwaves in Central Asia Under Climate Change

Abstract

Central Asia (CA) is a critical agricultural region, contributing significantly to global food and cotton production, yet it faces increasing threats from extreme heatwaves (HWs) due to global warming. Despite this, the specific impacts of historical and future HWs on CA's cropland remain underexplored. Here, using five bias‐corrected global circulation models from the Inter‐Sectoral Impact Model Intercomparison Project Phase 3b (ISIMIP3b), we present a detailed analysis of CA's cropland exposure to HWs from historical periods (1995–2014) and under three Shared Socioeconomic Pathways (SSP126, SSP370, and SSP585) for 2021–2100. Compared to historical levels, we find that exposure to heatwave frequency could increase by 199% by 2081–2100 under SSP126, while exposure to heatwave duration could rise by as much as 852% and 1143% under SSP370 and SSP585, respectively. Northern Kazakhstan emerges as particularly vulnerable, with the highest exposure levels across scenarios. Interactive effects between climate shifts and land‐use changes are the dominant contributors, accounting for over 50% of total exposure in each scenario. These findings highlight CA's vulnerability to HWs under various climate pathways, emphasizing the urgency of targeted adaptation strategies to protect regional agricultural resilience and, by extension, global food security.

Introduction

Heatwaves (HWs) stand out as highly destructive natural phenomena, posing a significant threat to human life, the economy, and society at large (Newman & Noy, 2023; Wang, Tao, et al., 2023; Zhang, Wang, et al., 2022). HWs may cause various negative impacts, including but not limited to increasing mortality and morbidity, causing agricultural production losses, leading to ecosystem degradation and increasing the risk of forest fires (Abram et al., 2021; Cole et al., 2023; Ping et al., 2023). According to the Sixth Assessment Report (AR6) of the Intergovernmental Panel on Climate Change (IPCC) (IPCC et al., 2021), global warming has significantly intensified the frequency, duration, and severity of HWs worldwide, underscoring the widespread impacts of climate change on ecosystems and agriculture (Hirsch et al., 2021; Liu et al., 2022). Projections indicate an alarming increase in heatwave intensity (HWI), frequency, and duration, which is expected to exacerbate their detrimental effects on agriculture, particularly in vulnerable regions (Domeisen et al., 2023; Wang et al., 2018; Yin et al., 2023). These prolonged periods of extreme heat lead to accelerated soil moisture depletion, increased evapotranspiration, and crop stress, significantly reducing yields and threatening food security. As HWs become more frequent, there is a growing need for comprehensive risk assessments and mitigation strategies to protect agricultural systems (Dimitrova et al., 2021). Research on HW impacts provides crucial insights to inform adaptation and resilience strategies that can address the compounding challenges of climate change on agricultural productivity.

HWs disrupt crop growth, incurring substantial economic losses and endangering food security (Brás et al., 2021; Chakraborty et al., 2019; Feng et al., 2021). Meteorological factors, particularly temperature and precipitation, have a significant impact on agricultural production, with about one third of global crop yield changes attributed to climate change (Kroeger, 2023). HWs are one of the most destructive extreme weather phenomena, with consequences that include direct losses, indirect impacts and long-term lag effects (Mazdiyasni & AghaKouchak, 2015; Xu et al., 2024). These heat waves result in a noteworthy 9%–10% reduction in cereal yields (Lesk et al., 2016). HWs not only directly stress crops, reducing pollen viability and causing tissue damage, but also indirectly affect yields and exacerbate drought phenomena by depleting soil moisture and enhancing evapotranspiration (Siebert et al., 2017). Particularly during the crucial growing season of crops, HWs poses a substantial threat to crop viability, directly influencing crop yields (Fontana et al., 2015; Jagermeyr & Frieler, 2018; Siebers et al., 2015).

Central Asia (CA) is currently experiencing a warming trend that is more than twice the global average rate (Zhang et al., 2019). Consequently, CA has endured recurrent severe HWs over recent decades, leading to substantial agricultural losses and far-reaching social consequences (Beurs et al., 2015; Fallah et al., 2023). Notably, the study area faced extreme heat during the early growing season of 2021, resulting in crop withering and significant livestock casualties due to an inadequate water supply. This exacerbated desertification in the already arid region, contributing to the vulnerability of ecosystems (Jiang et al., 2023). In CA, where agricultural production significantly contributes to the gross domestic product (GDP) and employment (Swinnen et al., 2017), intensifying HWs not only directly impact crops but also pose multifaceted challenges to production, livelihoods, ecology, social stability, and regional security (Qiu et al., 2022; Reyer et al., 2017). The frequency of catastrophic HWs suggests that CA is susceptible to further agricultural and socioeconomic losses, amplifying issues of rural poverty and unsustainable natural resource management. Surprisingly, the intricate impacts of HWs on cropland in CA have not undergone thorough examination.

Recent research on HWs has predominantly focused on demographic, economic, and infrastructure exposures (Forzieri et al., 2018; Li & Zha, 2020; Ullah, Saleem, et al., 2022; Yin et al., 2022). Currently, numerous studies have employed fixed estimates of population or cropland to evaluate exposure to climate extremes (Chen et al., 2019; Smirnov et al., 2016). However, this approach lacks precision in predicting the consequences of climate extremes, as demographic characteristics is also important indicator of population exposure (Smirnov et al., 2016). This holds true for changes in cropland exposure as well. Despite the profound effects of HWs on 21st-century agriculture, existing studies often lack impact assessments on cropland. Considering the substantial economic disparities and shifts in cropland within CA, HWs present a formidable threat to food security. Consequently, investigating the variability of future heat wave events in CA and quantifying the risk associated with their exposure to cropland are imperative steps for developing policies aimed at risk reduction for climate adaptation. Furthermore, such investigations play a crucial role in safeguarding future food security.

Drawing upon the aforementioned considerations, this study aims the characteristics of HWs variability during the crop growing period by employing relative thresholds. Additionally, it anticipates the characteristics of cropland exposure in CA throughout the 21st century, utilizing data from the five latest contributions in the Inter-Sectoral Impact Model Intercomparison Project Phase 3b (ISIMIP3b) and projections of cropland area from Land-Use Harmonization 2 (LUH2). The primary objectives of paper: first, to enhance the comprehension of HWs in CA; second, to delineate high-risk areas impacted by HWs under diverse future scenarios. Furthermore, the study aims to thoroughly assess and quantify the relative contributions and compounding effects of both climate and cropland on future cropland exposure within the study region. The study helps predict future trends in agricultural production and provides decision support for long-term planning of agricultural production.

Materials and Methods

Climate Data

We used bias-corrected forcings from five global circulation models (GCMs) in the latest release of the Inter-Sectoral Impact Model Intercomparison Project phase 3b (ISIMIP3b) (), with details on each model provided in Table S1 in Supporting Information S1. The models capture a range of emissions scenarios across three Shared Socioeconomic Pathways (SSPs): low emissions (SSP126), medium emissions (SSP370), and high emissions (SSP585), representing varied socioeconomic and emissions trajectories (Yun, Tang, Sun, & Wang, 2021). Comprehensive descriptions of each scenario are available in Table S2 in Supporting Information S1. These GCMs, selected to represent the Coupled Model Intercomparison Project 6 (CMIP6) ensemble, were bias-corrected and statistically downscaled to 0.5° × 0.5° resolution using the ISIMIP3BASD method and W5E5 observational dataset, both of which ensure robust correction of GCM biases while preserving trends, particularly in extreme climate events (Cucchi et al., 2020; Lange, 2019; Yun, Tang, Li, & Wang, 2021). The ISIMIP3b bias adjustment is tailored for climate impact studies and has been validated extensively in recent studies (He et al., 2023; Jiang & Zhou, 2023; Liu et al., 2021; Reyniers et al., 2023; Yun, Tang, Sun, & Wang, 2021). The dataset employed for the reference period in this study spans from 1995 to 2014, while the future projection period extends from 2021 to 2100. The future period is subdivided into four distinct time intervals (2021–2040, 2041–2060, 2061–2080, and 2081–2100) to maintain consistency with the historical period. The historical climate model data were evaluated using the Climatic Research Unit gridded Time Series version 4.07 (CRU TS v4.07) dataset (). To address inherent uncertainties associated with relying solely on the output of a single model, this study adopts integrated means derived from selected models for each scenario (Liu et al., 2020).

Land-Use Data

To assess cropland exposure to the risk of HWs, this study utilized the LUH2 gridded cropland dataset for both historical and future assessments () (Ma et al., 2020). Land-Use Harmonization 2, designed for CMIP6, integrates historical and projected future land use data based on the Historical Global Environmental Database (HYDE) and aligns with the Shared Socio-Economic Pathways (SSP) (Chen, Vernon, et al., 2020; Hurtt et al., 2020), Spanning from 850 to 2100, LUH2 offers year-by-year land use data at a spatial resolution of 0.25° × 0.25°, including globally gridded partial land-use patterns, potential transitions, and essential information on agricultural management (Song et al., 2021). Cropland data were extracted for three scenarios: SSP126, SSP370, and SSP585, covering the historical period (1995–2014) and the future timeframe (2021–2100), the detailed crop types is shown in Table S3 in Supporting Information S1. To align with climate data, these datasets underwent bilinear interpolation, resulting in a consistent resolution of 0.5° × 0.5°.

Definition and Characteristics of HWs

In the current context of rapid climate change, the impacts of HWs on ecosystems represent a critical concern, particularly for global food security. However, a universally accepted definition of the conditions constituting a HW has yet to be established. Two primary approaches are commonly applied to define HWs, each with geographic specificity (Zuo et al., 2015). An approach centers on human thermoregulation, termed the absolute threshold, measured with fixed values. Conversely, an alternative approach, emphasizing adaptation to local climatic conditions, employs percentile-based (relative) thresholds (Dong et al., 2021; Ullah et al., 2023). Considering the climatic conditions and geography of CA, the latter method offers a more accurate reflection of HWs in the region than fixed values. The definition and calculation of HW events in this study adhere to methodologies established in previous studies (Chen, Chen, et al., 2023; Nissan et al., 2017; Sun, Zou, et al., 2023; Zhang et al., 2020). In this study, a HW was defined as a period of three or more consecutive days during which the daily maximum temperature exceeds the 95th percentile of historical baseline temperatures (1995–2014) for each grid cell (Chen, Li, et al., 2020; Dong et al., 2021). This threshold was calculated based on temperatures from April to October, reflecting the main crop-growing season (Diffenbaugh et al., 2021; Nilahyane et al., 2020). To characterize heatwave variability, three metrics were used (Table 1): heatwave intensity (HWI), defined as the average maximum temperature across all days within each HW event; heatwave frequency (HWF), representing the average annual number of HW events; and heatwave duration (HWD), calculated as the average number of consecutive days per HW event (Wang et al., 2024).

Table 1 Definition of Heatwaves Characteristics

Type	Definition	Units
Heatwave Intensity (HWI)	Mean maximum temperature across all days within each heatwave event	°C
Heatwave Frequency (HWF)	Mean annual count of heatwave events	event
Heatwave Duration (HWD)	Mean number of consecutive days within each heatwave event	days

Cropland Exposure to HWs

To underscore the vulnerability of cropland to HWs, the extent of cropland exposed to the impacts of HWs at each grid point was systematically quantified. Cropland exposure was determined by multiplying the cropland area by the number of HWs at each grid point (Ullah, Saleem, et al., 2022; Ullah, You, et al., 2022; Wang, Li, et al., 2023). Given that HWs are examined on a daily scale in this study, the unit of cropland exposure is expressed in square kilometers (Wang, Zhao, et al., 2023). For each GCM projection, the 20 year average exposure was computed during different time intervals under diverse SSPs. The formula for calculating cropland exposure is provided below: 1 $${E}_{crop}=\frac{\sum \nolimits_{n=1}^{20}{HW}_{n}\times C}{20}$$ where $${E}_{crop}$$ represents cropland exposure (km²), C represents the cropland, $$n$$ denotes the $$n$$ th year of the defined study period, $${HW}_{n}$$ represents the annual changing characteristics of HWs.

Relative Changes of the Exposure Factors

The changes in exposures ($$\mathit{{\increment}}{E}_{crop}$$) related to climate, cropland, and their interactions were dissected into climate effects, cropland effects, and interaction effects, respectively (Chen & Sun, 2021). To gauge the relative change attributable to each effect, we adopted a methodology akin to that employed in prior studies (Zhang, Sun, et al., 2022). In essence, cropland effects were estimated by maintaining a constant climate while allowing variations in cropland. Conversely, in calculating climate effects, cropland was held constant. The interaction effect aimed to characterize the proximity of areas with growing cropland to HWs under changing climatic conditions. The calculated as follows: 2 $$\mathit{{\increment}}{E}_{crop}={HW}_{H}\times \mathit{{\increment}}C+{C}_{H}\times \mathit{{\increment}}HW+\mathit{{\increment}}C\times \mathit{{\increment}}HW$$ where $${C}_{H}$$ represents the historical cropland, $${HW}_{H}$$ is the characteristics of HWs in historical periods; The terms $$\mathit{{\increment}}C$$ and $$\mathit{{\increment}}HW$$ denote future cropland change and the number of future HW events occurrences, respectively. The contribution rate of each factor can be computed as follows: 3 $${CR}_{cli}=\frac{{C}_{H}\times \mathit{{\increment}}HW}{\mathit{{\increment}}{E}_{crop}}\times 100\mathit{\%}$$ 4 $${CR}_{crop}=\frac{{HW}_{H}\times \mathit{{\increment}}C}{\mathit{{\increment}}{E}_{crop}}\times 100\mathit{\%}$$ 5 $${CR}_{\mathit{int}}=\frac{\mathit{{\increment}}C\times \mathit{{\increment}}HW}{\mathit{{\increment}}{E}_{crop}}\times 100\mathit{\%}$$ where $${CR}_{cli}$$, $${CR}_{crop}$$ represent the contribution rates of climate changes and cropland changes, respectively. $${CR}_{\mathit{int}}$$ shows the contribution rate of cropland-climate interaction.

Results

Characterization of Changes in HWs

Initially, we assess the performance of the ISIMIP3b model ensemble in simulating daily maximum air temperatures during the historical period (1995–2014) by comparing model outputs with observed values from the CRU dataset (Figure S1 in Supporting Information S1). The spatial and temporal distribution of daily maximum temperatures in CA is effectively captured by all climate models. Mirroring the observed pattern, the modeled centers of high-temperature values are situated in Uzbekistan and Turkmenistan in the southwestern region, while low-temperature values prevail in the mountainous regions of northern Kazakhstan, Kyrgyzstan, and Tajikistan. However, a minor underestimation of daily maximum temperatures, approximately 3°C, is noted in the central part of Kazakhstan across all climate models. This underestimation is consistent across individual models (Figure S2 in Supporting Information S1). Despite this localized underestimation, the spatial correlation of the multi-model ensemble results exceeds 0.95, and the temporal correlation surpasses 0.99 when compared to observed values, both significant at the 0.01 level. Consequently, we employ the ensemble median to represent the projected changes in HW characteristics throughout.

In the context of global warming, HWs changes very significant in recent decades. Projections under these scenarios indicate a substantial increase in HWs in CA in the near future, depicting a noteworthy elevation in the intensity of HWs from low (SSP126) to high emission scenario (SSP585) (Figure 1a–1c). Conversely, a surge in HWs becomes more pronounced during the mid-to late 21st century. Analyzing frequency and duration, the number of HWs days exhibits an upward trend, particularly in the medium (SSP370) and high emission scenarios (SSP585) (Figure 1d–1i). Under the SSP126 scenario, early mitigation efforts demonstrate a moderating effect on HW characteristics, with the intensity, frequency, and duration of HWs stabilizing from 2060 onwards, highlighting the potential benefits of early intervention on climate stability in CA.

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HWs characteristics in CA reveal distinct regional patterns across historical and projected periods (Figure 2). Historically, HWI has been most pronounced in Turkmenistan, although events surpassing 40°C have been uncommon. In east-central Kazakhstan, HWs occur frequently, with more than three events annually, while in Tajikistan, HWs are marked by extended durations, frequently lasting over 10 days (Figure S3 in Supporting Information S1). Projections indicate a significant escalation in HW characteristics over time and with increasing emissions. Under the SSP126 scenario, HWF shows a marked increase relative to the baseline, with frequencies in central-eastern Kazakhstan rising approximately threefold (Figure 2a). In the SSP370 scenario, both HWI and frequency amplify considerably, with temperatures exceeding 45°C projected for large portions of Turkmenistan and eastern Uzbekistan by 2081–2100 (Figure 2b). Additionally, HWD is expected to expand substantially, with durations quadrupling compared to the baseline period, particularly in central-southern CA, covering a notably broader region. Under the SSP585 scenario, HWD is projected to increase by a factor of five from the baseline, with HWs lasting over 40 days and reaching intensities above 45°C across south-central CA by the late 21st century (Figure 2c). Although HWF does not exhibit substantial increases under higher emission scenarios, the projected HWD intensifies by factors of 4–5 compared to the baseline, suggesting that CA is likely to experience HWs of significantly longer duration and higher intensity in future scenarios.

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Changes in Cropland

The spatial distribution of cropland in CA is highly uneven, concentrated predominantly in northern and eastern Kazakhstan and along the border regions of Tajikistan, Uzbekistan, and Kyrgyzstan (Figure 3). During the historical period, cropland area in CA generally exhibits an increasing trend, with a spatial pattern comparable to that projected for the future period (Figure S4 in Supporting Information S1). However, under the SSP126 and SSP370 scenarios, the cropland is projected to decline in each of the time periods up to 2060, only to rebound after 2060. In contrast, under the SSP585 scenario, CA's cropland area tends to rise initially and then decline. Results indicate that under the SSP370 scenario, cropland area in CA is projected to undergo substantial changes, ranging from 10,000 to 40,000 square kilometers in almost all periods (Figure 3e–3h). This is followed by SSP126, where significant changes in cropland are projected in the near and medium term, with relatively smaller changes in the long term (Figure 3a–3d). The reduction of cropland area in SSP370 scenario may be attributed to land desertification, national policies and unsustainable development practices. For instance, historical evidence points to Kazakhstan abandoning part of its agricultural land around the eastern Aral Sea in 2003 to prevent soil desertification. Additionally, long-term agricultural irrigation in the Aral Sea region in 2018 resulted in the shrinkage of the Aral Sea to 13% of its 1960 area (Jiang et al., 2019; Zhang, Hajat, et al., 2022). Given these precedents and the emphasis on state policies and environmental conservation, a future reduction in CA's agricultural land appears likely (Han et al., 2022; Mirzabaev et al., 2023).

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Cropland Exposure to HWs

Changes in cropland exposure to HWF under different future climate scenarios are illustrated in Figure 4. Under the SSP126 scenario, cropland exposure shows a consistent upward trend, peaking in 2081–2100 at approximately 199% above the historical period (Figure S5 in Supporting Information S1). For SSP370, cropland exposure initially increases, reaching its highest level in 2041–2060 with an increase of around 155% over the baseline, before beginning to decline. Similarly, under SSP585, exposure follows an initial increase, peaking at 2041–2060 at about 190% above the baseline, and subsequently decreases. Spatially, areas of high cropland exposure to HWF are predominantly concentrated in northern Kazakhstan across all scenarios. In SSP126, increased exposure remains primarily in this region (Figure 4a–4d). Under SSP370, however, elevated exposure extends to eastern Uzbekistan and Turkmenistan, highlighting additional regions of vulnerability (Figure 4e–4h). SSP585 similarly shows intensified exposure in northern Kazakhstan. Overall, cropland in northern Kazakhstan emerges as particularly sensitive to increasing HWF (Figure 4i–4l), underscoring this region's heightened vulnerability under future climate scenarios.

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Changes in cropland exposure to HWD under various future SSP scenarios are presented in Figure 5. Unlike the trends observed for HWF, cropland exposure to HWD under SSP126 shows no significant change. However, under SSP370 and SSP585, cropland exposure to HWD in CA exhibits a substantial upward trend, with higher emissions scenarios corresponding to larger areas of cropland exposed to extended HWs. Peak exposure occurs in 2081–2100, with increases of 852% and 1143% over the baseline for SSP370 and SSP585, respectively (Figure S6 in Supporting Information S1). Notably, differences in exposure between scenarios become more pronounced over time. Spatially, high-exposure regions for HWD are concentrated primarily in northern Kazakhstan and southeastern CA. Under increased emissions scenarios, the extent and intensity of these high-exposure areas expand significantly. Overall, cropland in northern Kazakhstan is most affected by HWD, with southeastern and eastern regions of the study area also increasingly exposed to prolonged HWs.

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Relative Changes in Exposure Factors

Initially, we describe the effects of each factor by precalculating climate and cropland exposure while holding one variable constant. The definition of interaction effects is based on simultaneous changes in climate and cropland. The factors influencing changes in cropland exposure to HWF under different future climate scenarios are presented in Figure 6. Under SSP126 (Figure 6a), the interaction effect between climate and cropland changes is dominant across all four future periods, contributing between 52.89% and 57.34%, with a general pattern of decreasing initially and then increasing over time. Cropland alone exhibits a trend of initial increase followed by a decline, whereas the effect of climate mirrors the trend of the interaction effect but with a lower contribution (7.31%–11.75%). Under SSP370 (Figure 6b) and SSP585 (Figure 6c), the contributions of these factors follow similar patterns. The interaction effect increases initially before decreasing, while the cropland contribution decreases initially and subsequently rises. The impact of climate alone remains relatively stable, with its contribution rate slightly higher under SSP370 (10.81%–15.07%) compared to SSP585 (7.01%–11.16%). Cropland alone surpasses the interaction effect only in the 2021–2040 period, after which the interaction effect consistently dominates. In summary, the interaction between climate and cropland changes is the predominant factor influencing shifts in cropland exposure to HWF across scenarios, with the exception of the 2021–2040 period.

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The factors influencing changes in cropland exposure to HWD under different future climate scenarios are shown in Figure 7. Across all three climate scenarios, the temporal trends of the three factors—interaction, climate, and cropland—are notably consistent. Both the interaction and climate effects exhibit a decline followed by an increase, whereas the cropland effect shows an increase before declining. In SSP126 (Figure 7a), the cropland factor surpasses the interaction effect only during 2041–2060, while the interaction effect dominates during all other periods. The climate factor contributes between 4.45% and 8.6%, following a similar trend to the interaction effect but at a substantially lower magnitude (41.65%–54.38%). Under SSP370 (Figure 7b) and SSP585 (Figure 7c), the trends for the three factors remain consistent, though the climate contribution is somewhat higher under SSP370 (8.29%–45.65%) compared to SSP585 (6.4%–44.2%). Additionally, the interaction effects contribution under SSP585 (49.24%–79.26%) is slightly higher than under SSP370 (46.29%–74.48%). As emission scenarios intensify, the gap between the contributions of the interaction effect and cropland effect widens. Overall, the interaction effect is the primary factor influencing cropland exposure to HWD across all scenarios.

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Discussion

The spatial variability of HWs occurrences across CA shows pronounced impacts under SSP scenarios, highlighting the significance of HWF at a regional scale. HWs in this study are defined as periods exceeding the 95th percentile temperature threshold, capturing impactful regional events without the need for extreme absolute temperatures (Sun, Ge, et al., 2023). This threshold-based approach addresses the challenge of overlooking climate change impacts at high latitudes and altitudes, making it a widely accepted method for HWs identification (Barcena-Martin et al., 2019). While increasing extreme precipitation is predicted for CA, potentially influencing humidity and HW characteristics (Wei et al., 2023; Yao et al., 2021), our study centers on HW impacts, given their acute effects on cropland productivity in this predominantly arid region. Some scholars have already considered the humidity element for identifying HWs. Additionally, recent investigations have explored the effects of heat induction on labor productivity using human safety and health criteria, employing the Hot-haps function to define HWs (Chen, Vernon, et al., 2020; Cheng et al., 2023; Krstic et al., 2017). However, because this study centers on HW impacts specifically on cropland, humidity was not integrated into our HWs assessments.

Our focus on HWs, rather than droughts or compound events, stems from CA's distinctive climate dynamics, where annual droughts are common, and crops have developed a certain level of drought resilience (Qin et al., 2021). In contrast, high temperatures during HWs present a distinct and acute stressor, with limited crop tolerance to sustained heatwaves (Wang, Tao, et al., 2023; Yu et al., 2020). CA's semi-arid zones are particularly prone to HW-induced transpiration stress, which is more detrimental to crop yields than drought in isolation. While flash drought, compound drought-flood events are less common in CA, understanding HW impacts independently addresses a critical research gap in this region. However, future studies could incorporate socioeconomic and ecological perspectives on drought and compound hazards to develop a more comprehensive risk assessment.

Our results reveal a nuanced relationship between climate scenarios and cropland exposure to HW characteristics. Although HWI is projected to increase across the region, these increases are concentrated in desert areas with limited cropland, where exposure risk remains low. In contrast, cropland exposure to prolonged HWD increases significantly under future scenarios. Northern and southeastern CA, areas dense in agricultural activity, are projected to experience extended HW periods, leading to higher exposure. Northern Kazakhstan, in particular, emerges as a hotspot for cropland exposure across multiple HW metrics, marking it as a priority region for targeted adaptation measures. These exposure patterns underscore the combined influence of climate and land-use changes, particularly under high-emission scenarios, where greater HWI and HWD variability are expected. Such findings highlight the need for integrated land-use planning and climate mitigation efforts to manage cropland vulnerability effectively.

Relevant studies indicate that HW impacts vary substantially across crop growth stages, with periods of germination, flowering, and grain filling particularly sensitive to HWs (Hassan et al., 2024; Lobell et al., 2015). For example, HWs during flowering may reduce grain set, while late-stage HWs can induce premature drying, compromising yield and quality in staple crops such as wheat, barley, and maize (David et al., 2021; Ullah et al., 2024). Given the heavy reliance of CA populations on these crops, the socioeconomic implications are substantial, directly impacting food security. Adaptive strategies, including the development of heat-tolerant crop varieties and adjustments to planting schedules, could mitigate the adverse effects of HWs on crop yield stability.

Although climate projections provide critical insights for decision-making, they are influenced by factors such as model parameter variations, internal and external forcings, and inherent uncertainties. Thus, projection outcomes should be interpreted cautiously, avoiding deterministic conclusions (Zhao et al., 2021; Zheng et al., 2021). Presently, few efforts have been made to evaluate uncertainty in prior studies on this subject, and reliable methods for quantifying uncertainty are lacking, presenting a major challenge in climate projection. In the subsequent phases of our research, emphasis will be placed on determining a reliable method to evaluate the uncertainty of exposure prediction of HWs. Simultaneously, recognizing that HWs exposure is time-dependent due to cropland variability, our future endeavors will encompass the consideration of rain-fed agriculture, irrigated agriculture, and specific crop types to inform tailored adaptation strategies. It is noteworthy that while our utilization of pooled medians effectively mitigated model uncertainty, some bias may persist in this study. Subsequent studies should explore the application of weighted averaging or employ deep learning techniques to more comprehensively evaluate how climate models respond to diverse emission scenarios and is anticipated to offer a valuable pathway and reference for addressing the challenge of projection bias in future assessments (Chen, Leung, et al., 2023; Liu et al., 2023).

Previous studies often relied on fixed cropland assumptions and less precise future climate scenarios (Arnell & Gosling, 2016; Huang et al., 2021). By incorporating updated CMIP6 models and dynamic factors, our study provides a more accurate estimation of future HW impacts on CA cropland. Moving forward, adaptation strategies should focus on both climate mitigation and flexible land-use planning to account for these complex interactions and minimize HW vulnerability. Comprehensive analyses should incorporate various natural and anthropogenic factors, acknowledging the complex interactions within social and economic systems. Further, a systematic examination of HW processes, surface drivers, feedback mechanisms, and compounding effects in arid zones remains essential. Future research should integrate considerations of humidity, health, and specific agricultural types to refine HW impact assessments and enhance their regional specificity (Ridder et al., 2020).

Conclusions

This study is distinctive in being the first to analyze projected changes in HWs in CA during the future growing season, utilizing simulations from the recently released ISIMIP3b model. It also pioneers the projection of future cropland exposure to HWs in CA. The primary findings are outlined below.

• Areas vulnerable to the risk of HWs in CA are concentrated in northern Kazakhstan, southeastern Uzbekistan, and south-central Turkmenistan.

• The regions at risk of HWs exhibit a clear trend of escalation and expansion in CA under different scenarios. This change intensifies from the low to high emission scenario, with a particularly pronounced increase in the HWI and HWD.

• Under the three climate scenarios, future heat wave events (HWI, HWD, and HWF) in CA generally display a gradual increasing trend, with SSP126 showing the least increase and SSP585 the most pronounced.

• Increases in cropland exposure to HWF and HWD across CA under future climate scenarios, with the effects most pronounced in high-emission pathways. Under SSP126, cropland exposure to HWF and HWD shows a moderate upward trend, while SSP370 and SSP585 project considerable increases, particularly in the mid-to late-21st century. Specifically, cropland exposure to HWD peaks at over 1140% above historical levels under SSP585, underscoring the significant risk that prolonged HWs pose to regional agriculture as emissions intensify. Spatial analysis identifies northern Kazakhstan as particularly vulnerable to increasing HWF and HWD, with high-exposure zones extending to eastern Uzbekistan, Turkmenistan, and southeastern CA in high-emission scenarios.

• Across all scenarios, the interaction between climate and cropland changes is the dominant factor driving shifts in cropland exposure, accounting for over half of the total impact, with contributions reaching up to 79.26% for HWD under high emissions scenarios (SSP585). In lower emissions scenarios (SSP126), although climate and cropland individually influence exposure, the interaction effect remains prominent, suggesting that simultaneous changes in land use and climate exert a compounding influence on cropland vulnerability. Notably, while cropland factors temporarily surpass interaction effects in certain periods (such as 2021–2040 for HWF and 2041–2060 for HWD under SSP126), the long-term trend shows interaction effects resuming dominance as emissions increase.

Acknowledgments

The research was supported by the Key R&D Program of Xinjiang Uygur Autonomous Region (Grant 2022B03021), the Tianshan Talent Training Program of Xinjiang Uygur Autonomous region (Grant 2022TSYCLJ0011), the Transformation of Scientific and Technological Achievements from the Qinghai Province (Grant 2020-SF-145), the 2020 Qinghai Kunlun talents -Leading scientists project (Grant 2020-LCJ-02), the Key program of International Cooperation, Bureau of International Cooperation, Chinese Academy of Sciences (Grant 131551KYSB20210030) and Tao Li was supported by a grant from the program of China Scholarship Council (ICPIT–International Cooperative Program for Innovative Talents, Grant 202310630003) during his study in Ghent University, Ghent, Belgium.

Conflict of Interest

The authors declare no conflicts of interest relevant to this study.

Data Availability Statement

Historical and future climate model data are publicly available at the following links: . The data used for the assessment of future climate models comes from CRU TS 4.07 and is publicly available at the link below: . Historical and future land use data is from LUH2 and is publicly available at the link below: .

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