Study on the Synergistic Mechanisms of Daytime and Nighttime Heatwaves in China Based on Complex Networks

Abstract

Heatwaves pose increasing risks to human health and socio-economic systems, yet their spatiotemporal organization and underlying synergistic mechanisms remain insufficiently understood, particularly with respect to daytime and nighttime processes. Using a dual identification framework combining absolute and relative temperature thresholds, this study systematically investigates the spatiotemporal evolution of daytime and nighttime heatwaves across China during 1961–2022. A complex network approach is further introduced to characterize the interannual co-variability and interdecadal structural evolution of heatwave activity from a system-level perspective. Results reveal a pronounced interdecadal transition in the early 1990s, accompanied by a fundamental reorganization of heatwave co-occurrence networks. Heatwave frequency exhibits a clear post-transition desynchronization, characterized by a sharp decline in network connectivity and fragmented local clustering, indicating a shift from large-scale, circulation-dominated coherence toward increasingly localized and heterogeneous heatwave occurrences. In contrast, heatwave duration shows an opposite evolution, with significantly enhanced spatial synchronization after the transition. Degree centrality and clustering coefficients increase markedly, and high-connectivity cores expand from coastal regions into inland areas, including North, Central, and Northwest China. This coexistence of desynchronized heatwave occurrence and strongly synchronized persistence suggests an emerging high-risk regime in which heatwaves occur more randomly but, once initiated, tend to persist coherently across large regions. Furthermore, a dual-layer network analysis reveals previously undocumented cross-temporal coupling between daytime and nighttime heatwaves, with pronounced regional differences. The middle and lower reaches of the Yangtze River are more strongly influenced by local processes, whereas northern China is increasingly governed by large-scale circulation control and enhanced regional clustering after the transition. These findings demonstrate that complex network analysis provides a powerful framework for uncovering hidden structural changes in extreme heat events and offer new insights into the evolving risks of compound and persistent heatwaves under climate change.

1. Introduction

In recent years, increasing attention has been given to the differentiated characteristics of daytime heatwave (DHW) and nighttime heatwave (NHW) [1]. Existing studies indicate that DHWs are typically associated with intense solar radiation, dry land surfaces, and persistent subsidence airflow [2], with their primary impacts concentrated in outdoor labor, agricultural activities, and energy demand [3,4,5]. In contrast, NHWs occur when nighttime minimum temperatures remain abnormally high and are often linked to atmospheric inversions, intensified urban heat island effects, moisture accumulation, and large-scale stagnant weather systems [6,7]. Numerous studies have shown that NHWs pose a greater threat to human health than DHWs, as elevated nighttime temperatures hinder physiological recovery and substantially increase the risk of cardiovascular and respiratory diseases [8,9]. From a spatiotemporal perspective, the global increase in NHWs has generally outpaced that of DHWs, with the trend being particularly pronounced in East Asia, especially in China [10]. Research further suggests that the rate of increase in nighttime extreme temperatures in eastern and southern humid regions of China exceeds that of daytime extremes, potentially due to regional moisture enhancement, urban expansion, and changes in nighttime boundary-layer structures [10]. Therefore, it is essential to conduct a comprehensive analysis of daytime and nighttime heatwaves in China.

Existing research on heatwaves in China has mainly focused on two aspects: characterizing their spatiotemporal statistical features and revealing the mechanisms underlying their occurrence and evolution. Regarding feature analysis, numerous studies have systematically assessed the fundamental spatiotemporal patterns and long-term trends of heatwaves in China based on metrics such as onset and end dates, frequency, duration, and intensity [11,12,13,14]. In terms of causal mechanisms, most studies adopt a large-scale atmospheric circulation perspective, indicating that heatwave development in China is jointly influenced by the Western Pacific subtropical high, the Tibetan Plateau high, Eurasian teleconnection patterns, and air–sea interactions [15,16,17,18]. Meanwhile, land-surface changes induced by human activities and rapid urbanization can modify local energy and water budgets by enhancing sensible heat fluxes and suppressing evapotranspiration, thereby increasing near-surface air temperature and intensifying the urban heat-island effect [19,20]. Such local thermal anomalies not only elevate the background state for extreme heat occurrence but can also further modulate the development and evolution of heat-wave events by affecting boundary-layer structures and their interactions with large-scale circulation as represented by General Circulation Models (GCMs), acting as an important complement to large-scale dynamical forcing. Although these studies have provided comprehensive insights into heatwaves in China from both statistical and dynamical perspectives, they remain largely focused on “overall heatwaves” or those dominated by daytime maximum temperatures.

Recent studies have indicated that DHW sand NHWs exhibit significant differences in their formation mechanisms, spatiotemporal structures, and societal impacts [21,22,23]. Most existing research has focused on overall trends in heatwaves or the mechanisms of a single heatwave type, lacking a systematic exploration of the dynamic relationships between DHW and NHW. In particular, their synergistic evolution at regional scales, the pathways through which mutual influences propagate, and the potential existence of stable cooperative patterns remain poorly understood. Moreover, although complex-network methods have increasingly been applied to analyze structural features and teleconnections within climate systems—revealing deep and hidden correlations [24]—their application to a dual-layer DHW–NHW complex network, aimed at uncovering the synergistic interactions between daytime and nighttime heatwaves, has not yet been reported.

Based on long-term meteorological observations in China from 1961 to 2022, this study constructs a dual-layer complex network of DHW and NHW to systematically analyze their interannual variability and structural connections among regions. By quantifying network centrality and structural roles of different regions in the cooperative occurrence of heatwaves, the study aims to identify key nodes and core areas in China’s heatwave distribution and to reveal the potential synergistic features and spatiotemporal evolution patterns of DHW and NHW. The results not only deepen the understanding of the mechanisms underlying the spatiotemporal distribution of heatwaves in China but also provide a novel theoretical perspective and methodological framework for future analyses of extreme high-temperature events using network-based approaches.

2. Data and Methods

2.1. Data

The dataset used in this study is the daily gridded surface temperature product (0.5° × 0.5°, Version 2.0) directly provided by the China Meteorological Administration (CMA). It is based on in situ observations from nationwide meteorological stations, quality-controlled and homogenized before interpolation onto the gridded product. The data cover 1961–2022, with a total of 4189 grid points. To consistently define and identify major summer heatwave events over China, the analysis period is restricted to May–September each year, which corresponds to the peak season of high-temperature events under the influence of the East Asian summer monsoon. All subsequent heatwave identification, indicator calculations, and complex network analyses are conducted using this period.

2.2. Methods

2.2.1. Definition of Heatwaves

Existing studies have not established a unified definition standard for heatwaves [25,26,27]. Most previous research has defined heatwaves using relative thresholds [8,28]; however, relative thresholds often overlook the physiological impacts of extreme temperatures. Therefore, in this study, absolute thresholds are introduced in addition to relative thresholds for defining heatwaves. The definition of heatwaves is as follows:

• Daytime Heatwave (DHW): A period of at least three consecutive days during which the daily maximum temperature exceeds both the 90th percentile of the daily maximum temperature for May–September over 62 years at a given grid point and 30 °C.
• Nighttime Heatwave (NHW): A period of at least three consecutive days during which the daily minimum temperature exceeds both the 90th percentile of the daily minimum temperature for May–September over 62 years at a given grid point and 20 °C [28].

Heat waves were identified using a combination of absolute and percentile-based temperature thresholds, together with a minimum duration criterion. Absolute thresholds of 30 °C for daytime and 20 °C for nighttime were applied to ensure physiological relevance, as these temperatures are commonly associated with increased heat-related mortality and morbidity in previous studies [29,30]. Fixed thresholds also provide a consistent basis for comparison across different climatic regions, avoiding the classification of events that are relatively extreme but absolutely mild in cooler areas.

To account for local climate variability and seasonal differences, the 90th percentile of daily maximum (daytime) and minimum (nighttime) temperatures was also applied. Percentile thresholds were calculated over the full 62-year period, including long-term warming trends. The combined use of absolute and percentile thresholds allows the identification of both physiologically meaningful and relative extreme heat-wave events, improving robustness across regions and time scales.

A minimum duration of three consecutive days was used to define heat waves, consistent with the literature. Although this criterion may exclude sequences with brief interruptions, a sensitivity analysis allowing one-day gaps showed that the main spatial and temporal patterns of heat-wave occurrence remain largely unchanged, supporting the robustness of the adopted definition.

2.2.2. Heatwave Analysis Indicators

In previous studies, heatwave metrics have included the number of heatwave days, heatwave intensity, and heatwave duration [22]. In this study, two characteristics—number of heatwave days and number of heatwave events—are selected to analyze the distribution features of heatwaves in China. Their definitions are as follows:

• Number of Heatwave Days (NHWD): The total number of days on which nighttime heatwaves occur during May–September each year.
• Number of Heatwave Events (NHW): The total number of nighttime heatwave occurrences during May–September each year.

2.2.3. Complex Network

This study employs a complex-network approach to systematically analyze daytime and nighttime heatwaves across mainland China. Complex networks are a mathematical and computational framework used to describe the elements of a complex system (nodes) and the interactions among them (edges), allowing for the identification of internal structural features and association patterns among different components. As a result, complex networks have been widely applied in climate science. Compared with traditional statistical methods, complex networks offer a unique advantage in identifying key regions, transmission pathways, and cooperative behaviors from a holistic structural perspective, which is particularly valuable for studying the organizational patterns of extreme climate events. In existing climate network research, metrics such as node centrality, clustering coefficient, and path length are commonly used to characterize both local and global structural properties of networks. In this study, we focus on node centrality and clustering coefficient to represent the key nodes and local connectivity in China’s heatwave networks, thereby identifying the structural importance of different regions in the synergistic occurrence of daytime and nighttime heatwaves and revealing potential organizational patterns in heatwave distribution.

Node centrality measures the number of direct connections a node has within a network, reflecting its influence and importance. A higher degree centrality indicates that the node is directly connected to other nodes, and it is generally considered more important within the network. The degree centrality of a node is calculated as follows:

$${DC}_j={\sum }_{i=1}^N{A}_{ij},$$

(1)

where $N$ is the total number of nodes in the network, and $A_{ij}$ represents the network link between node $i$ and node $j$.

The clustering coefficient measures the degree of clustering or local cohesiveness of nodes within a network. It reflects whether a node’s neighbors are also interconnected, capturing the network’s “small-world” characteristics. A higher clustering coefficient indicates stronger local clustering in the network. The clustering coefficient is calculated as follows:

$${CC}_j={\displaystyle \frac{2\beta }{k_j\left(k_j-1\right)}},$$

(2)

where $k_j$ represents the number of nodes connected to grid point, while ${\displaystyle \frac{k_j-1}{2}}$ and $\beta$ denote the maximum possible number of connections among these nodes and the actual number of connections, respectively.

2.2.4. Detrending Procedure

To reduce the potential influence of long-term trends on the statistical analysis of extreme events, the time series of the NHW and NHWD were detrended. Specifically, linear regression was applied to the original annual-scale time series, with time as the independent variable and NHW/NHWD as the dependent variable, to fit a regression function representing the long-term trend. The fitted trend was then subtracted from the original series point by point to obtain the detrended anomaly series. This detrending procedure effectively removes low-frequency linear trend components while retaining the interannual variability of the heatwave indices, thereby mitigating the potential impact of long-term climate warming on subsequent statistical analyses and complex network construction, and enhancing the robustness of the results.

2.2.5. Network Construction

In this study, the construction and analysis of the complex network were carried out as follows:

• Identify the nodes of the heatwave network, which correspond to the valid grid points in the dataset.
• Compute the Pearson correlation coefficients between the 62-year time series of annual daytime heatwave occurrences across different regions, between the 62-year time series of annual nighttime heatwave occurrences, and between the 62-year annual time series of daytime and nighttime heatwave occurrences. The same procedure is repeated for the time series of heatwave days.
• Determine the effective edges of the network. If the p-value corresponding to the Pearson correlation coefficient between heatwave occurrences at two nodes is less than 0.05, an edge is considered to exist between the grid points; otherwise, no edge is established.
• Compute the network’s node centrality and clustering coefficient.

To analyze the synergistic relationships between the interannual variations in DHWs and NHWs across different regions, this study first constructs a daytime heatwave complex network (DHCN) and a nighttime heatwave complex network (NHCN), and then further develops a two-layer complex network (TLCN). The TLCN is defined as follows:

• Daytime Heatwave Complex Network (DHCN): Constructed based on the correlation coefficients of interannual daytime heatwave occurrences or heatwave days between different regions.
• Nighttime Heatwave Complex Network (NHCN): Constructed based on the correlation coefficients of interannual nighttime heatwave occurrences or heatwave days between different regions.

The nodes in each layer correspond to the same spatial grid points but represent the interannual time series of different types of heatwaves.

Interlayer edges are determined by calculating the correlations between corresponding nodes in the two layers. Specifically, this study uses the Pearson correlation coefficient to quantify the linear relationship between the interannual variations in daytime and nighttime heatwaves at the same grid point. If the correlation is statistically significant (p < 0.05), an interlayer edge is established between the corresponding nodes in the two-layer network.

In the two-layer network, this study focuses on the strength of interlayer connections between daytime and nighttime heatwaves. Accordingly, the following two interlayer connectivity metrics are defined:

• Daytime Heatwave two-layer Connectivity (TLCN-DH): Represents the strength of interlayer connections between a node in the daytime heatwave network and nodes in the nighttime heatwave network. It quantifies whether the interannual variation in daytime heatwaves in a given region are coordinated with nighttime heatwave variations over a broader area.
• Nighttime Heatwave two-layer Connectivity (TLCN-NH): Represents the strength of interlayer connections between a node in the nighttime heatwave network and nodes in the daytime heatwave network. It quantifies whether the interannual variation in nighttime heatwaves in a given region are influenced by or coordinated with daytime heatwave variations over a broader area.

2.2.6. Statistical Analysis

All complex network analyses were conducted using MATLAB R2020b (MathWorks, Natick, MA, USA). Network construction and computation of network metrics were implemented using custom MATLAB scripts based on built-in graph and matrix operation functions.

3. Results

3.1. Spatial and Temporal Distribution Characteristics of Heatwaves in China from 1961 to 2022

In the context of global warming, previous studies have reported a notable increase in heatwave intensity in China during the 1990s, particularly in western and northern regions [31]. To investigate the spatial distribution of heatwaves across China, this study analyzed the frequency and duration of heatwaves from 1961 to 2022. Figure 1 presents the overall spatial patterns of daytime and nighttime heatwave occurrences and durations across the study period, rather than temporal trends, highlighting the general distribution of heatwave events across the region.

Between 1961 and 2022, the number of heatwave days in China—including both daytime and nighttime heatwaves—exhibited pronounced spatial heterogeneity. Overall, regions with high heatwave frequency were mainly concentrated in southern and northwestern China. In these areas, the cumulative number of daytime and nighttime heatwave events over the 62-year period exceeded 90, and the total number of heatwave days surpassed 400. Within the spatial pattern of heatwaves in China, the middle and lower reaches of the Yangtze River stand out as the regions with the highest number of heatwave days and events. This area experienced at least 120 daytime heatwaves and 90 nighttime heatwaves, with most of Hubei Province experiencing at least 120 nighttime heatwaves. Alongside the high frequency of heatwave events, the number of heatwave days was also relatively high: over the 62 years, the middle and lower Yangtze region experienced at least 600 daytime and nighttime heatwave days, and some areas even exceeded 700 days for both daytime and nighttime heatwaves. Although northwestern China was a high-frequency region for daytime heatwaves, the number and duration of nighttime heatwaves were comparatively low, likely due to its unique climatic conditions, including aridity and large diurnal temperature ranges [32,33,34]. In contrast, in parts of northern China, nighttime heatwaves were generally less frequent and less intense than daytime heatwaves. Notably, no significant heatwave events meeting the defined criteria were recorded across the Tibetan Plateau in this study, which differs from some previous observational studies [21]. A key reason for this is the high-altitude characteristic of the region, where daily maximum and minimum temperatures rarely simultaneously exceed the absolute thresholds defined in this study for three consecutive days.

3.2. Heatwave Network Analysis

To systematically characterize the co-variability of heatwave occurrence frequency and heatwave days across different regions of China from 1961 to 2022, this study constructed a bilayer climate network consisting of daytime and nighttime heatwaves. Subsequently, we calculated the degree centrality and clustering coefficient for each node within the bilayer network to quantify interregional association strength and local structural features, thereby identifying key regions and organizational patterns in heatwave co-variability.

The degree centrality of the interannual co-occurrence networks of NHW and NHWD exhibits pronounced spatial heterogeneity, and the network connectivity corresponding to different heatwave metrics shows clear differences, reflecting regional variations in the synchronicity of heatwave changes with other areas (Figure 2). In the NHW co-occurrence network (Figure 2a–d), degree centrality generally follows a pattern of “high in central and eastern regions, low in western regions,” with high-value areas mainly concentrated in North China, the Huang–Huai region, the middle and lower reaches of the Yangtze River, and parts of Northeast China, whereas the Northwest and Southwest regions are generally low. This spatial pattern indicates that the interannual variations in heatwave frequency in central and eastern China are more strongly synchronized over a broader scale, while regions with complex terrain exhibit more regionally independent variations. The spatial positions of high-degree centrality regions are largely consistent across different network construction methods, suggesting the robustness of this feature. By contrast, the NHWD co-occurrence network (Figure 2e–h) shows a markedly higher overall degree centrality than the NHW network, with high-value areas expanding substantially, forming a relatively continuous distribution across North China, the Huang–Huai and Jiang–Huai regions, and southern Northeast China. This indicates that interannual variations in heatwave duration exhibit stronger nationwide synchronicity, possibly reflecting that persistent high-temperature events are more susceptible to common influences from large-scale circulation anomalies, such as the maintenance of the subtropical high [35,36]. Overall, heatwave days are more prone than heatwave frequency to exhibit coordinated changes across different regions. Central and eastern China consistently appears as a highly connected area in both networks, indicating that interannual variations in heatwave metrics at nodes in this region are more strongly synchronized with nodes across the country, rather than serving as “hubs” in a dynamical sense.

To further investigate the local clustering characteristics within the co-occurrence networks, we analyzed the distribution of clustering coefficients (Figure 3). In the NHW-based co-occurrence network (Figure 3a–d), clustering coefficients exhibit pronounced spatial heterogeneity. Central and eastern regions, including North China, the Huang–Huai region, the middle and lower reaches of the Yangtze River, and parts of Northeast China, generally show moderate to high clustering coefficients, indicating that interannual variations in heatwave frequency in these areas are not only strongly correlated with surrounding regions but also exhibit a high degree of internal consistency. In the NHWD-based co-occurrence network (Figure 3e–h), clustering coefficients are overall higher than in the NHW network, with high-value areas further expanding and forming a continuous distribution across North China, the Huang–Huai and Jiang–Huai regions, and southern Northeast China. This suggests that interannual variations in heatwave duration more readily form highly coordinated “correlation clusters” at the regional scale, meaning that adjacent or climatically similar areas are more likely to exhibit simultaneous changes in persistent high temperatures. The spatial patterns of clustering coefficients are generally consistent across different network construction approaches, indicating the robustness of these results. Compared to heatwave frequency, heatwave duration demonstrates stronger regional coherence and localized clustering across most of China, which may be associated with the common modulation of persistent high-temperature events by large-scale circulation anomalies and land–atmosphere feedbacks [37].

3.3. Changes in Heatwave Networks Before and After 1993

Previous studies have shown that heatwave activity over China experienced a pronounced interdecadal transition in the early 1990s, with distinctly different characteristics in heatwave days around 1993. Specifically, the number of heatwave days exhibited a decreasing trend prior to 1993, reached a minimum in that year, and increased thereafter [38]. To investigate whether and how this shift in the background climate state influences the spatial organization and connectivity of heatwave events, this study constructs daytime and nighttime heatwave complex networks for two subperiods: 1961–1993 (pre-transition) and 1994–2022 (post-transition). By comparing the topological structures of these networks, we aim to elucidate the evolution of spatiotemporal coordination patterns of heatwaves in China in response to changes in the climatic background.

By comparing the spatial distribution of degree centrality (DC) in the four types of heatwave co-occurrence networks before and after the abrupt change (Figure 4), we observe a significant evolution in the spatial synchronicity of heatwave frequency in China. During 1961–1993 (pre-shift), the nationwide heatwave networks exhibited pronounced large-scale coherence, with high-DC regions (1000–1600) concentrated in North, Central, and East China, areas strongly influenced by the monsoon (Figure 4a–d). This notable spatial connectivity indicates that, in this period, interannual fluctuations of heatwaves across eastern China were highly synchronized, and their occurrence patterns were primarily controlled by large-scale coherent atmospheric circulation anomalies. However, in 1994–2022 (post-shift), the network topology underwent a dramatic transformation. DC values across the country experienced a sharp decline, and the original high-value core regions in eastern China largely disintegrated into a low-value background (DC < 600; Figure 4e–h). This transition from “strongly correlated, large-scale synchronization” to “weakly correlated, locally fragmented” reveals a profound adjustment in the spatiotemporal evolution mechanisms of heatwaves. Although global warming has led to an overall increase in heatwave events, the influence of local feedback processes, uneven warming, and urbanization has become increasingly significant, resulting in a marked weakening of interregional heatwave connectivity [39,40]. This spatial desynchronization not only indicates that the dominant circulation signals driving heatwave evolution have become more complex but also suggests that regional heatwave prediction based on traditional large-scale teleconnection signals is becoming substantially more challenging [41].

To investigate the evolution of spatial synchronicity in heatwave days before and after the abrupt change, we conducted a similar analysis on the spatial distribution of DC in the four types of co-occurrence networks (Figure 5). In contrast to the “synchronicity collapse” observed in NHW networks, the heatwave days networks exhibit a pronounced trend of “enhanced connectivity and spatial expansion” during 1994–2022 (post-shift). During 1961–1993 (pre-shift), DC values across most regions were relatively low (generally below 600), with a scattered spatial distribution and no significant coherent core, indicating that the duration of early heatwaves was primarily driven by local meteorological factors independently (Figure 5a–d). However, in the post-shift period, the connectivity of the heatwave days networks increased markedly. In particular, in the NHCN and TLCN-DH networks, North China, Central China, and Southwest China formed large-scale, high-intensity DC cores, with DC values rising sharply to 1200–2000 (Figure 5e–h). This structural transition from a “locally independent” to a “large-scale synchronized” pattern suggests that, although the occurrence of heatwaves has become more scattered under the background of global warming, once heatwaves occur, their duration exhibits stronger spatial coherence. This implies that the circulation systems driving heatwave persistence have become more stable and extensive after the abrupt change, and that the cascading effects of local land–atmosphere feedback processes further enhance the coordinated fluctuations of heatwave days between regions, substantially increasing the systemic risk of large-scale, persistent heatwaves.

To reveal the differences in local clustering characteristics of NHW in China before and after the abrupt change, Figure 6 presents the spatial distribution of clustering coefficients (CC) in the four types of co-occurrence networks. During 1961–1993 (pre-shift), the nationwide heatwave networks exhibited relatively high clustering overall, with high-value areas (0.6–0.9) forming continuous distributions across North, East, and South China. This indicates that, in this period, heatwave fluctuations were not only synchronized at large scales but also formed tightly connected local “climate clusters.” However, in 1994–2022 (post-shift), the spatial pattern of clustering coefficients underwent significant reorganization. High-value clusters in the eastern monsoon regions became fragmented, while clustering coefficients increased in the Northwest and along the margins of the Tibetan Plateau, forming new localized correlation cores. This transition from a “broad, nationwide pattern” to a “regionally specific pattern” suggests a profound adjustment in the co-occurrence mechanisms of heatwaves after the abrupt change. The integrative effect of large-scale circulation weakened, whereas local forcing played a more dominant role in maintaining the internal synchronicity of heatwaves within specific regions, resulting in greater spatial heterogeneity in heatwave occurrence patterns.

To reveal the differences in local clustering characteristics of NHWD in China before and after the abrupt change, Figure 7 presents the spatial distribution of clustering coefficients in the four types of co-occurrence networks. During 1961–1993 (pre-shift), high clustering areas were mainly confined to North China and the southeastern coastal regions influenced by the monsoon, with a relatively isolated and limited spatial distribution. This indicates that early heatwave persistence exhibited local synchronicity only in specific climate-sensitive regions (Figure 7a–d). However, in 1994–2022 (post-shift), clustering coefficients showed a pronounced trend of inland expansion and overall strengthening. In particular, large-scale, high-intensity local correlation clusters emerged in the southern North China Plain, Central China, and Northwest China (Figure 7e–h). This transition from a “coastal-localized” to an “inland-expanded” pattern suggests a profound reorganization of the spatial structure of heatwave persistence after the abrupt change. The increased persistence of large-scale blocking high-pressure systems, combined with the cascading effects of local land–atmosphere feedbacks, collectively enhanced the statistical dependence of heatwave duration between regions. The strengthened local clustering implies that once persistent heatwaves occur, their spatial maintenance and evolution are more robust and coordinated, posing greater challenges for joint mitigation of extreme high temperatures across regions.

4. Discussion

This study provides a comprehensive analysis of the spatiotemporal evolution of daytime and nighttime heatwaves across China from 1961 to 2022, combining a dual identification criterion with a complex network framework. Compared with traditional regional statistics, this approach reveals the systemic organization and spatial connectivity of heatwaves, allowing for a more detailed understanding of both local and large-scale dynamics.

Our results show pronounced spatial heterogeneity in both heatwave frequency and duration, consistent with previous studies [21,42]. High-activity regions are concentrated in southern China, the middle and lower Yangtze River, and the northwest. Among these, the Yangtze River Basin emerges as a core hotspot, exhibiting the highest frequency and longest duration for both daytime and nighttime heatwaves [43]. In contrast, the northwest experiences frequent daytime heatwaves, but nighttime events are shorter and less frequent, likely due to the region’s arid climate and large diurnal temperature variations. Similarly, northern China shows weaker nighttime heatwaves compared with daytime events, reflecting the influence of large-scale atmospheric circulation patterns [42]. These spatial patterns confirm that heatwave characteristics in China are strongly region-dependent, in agreement with earlier regional analyses.

The two-layer climate network analysis highlights distinct patterns in interannual co-variability. While NHW exhibits high connectivity in central and eastern China, NHWD shows stronger overall connectivity and clustering, forming continuous high-value belts across North China and southern Northeast China. This suggests that persistent heat events are more spatially coherent than event frequency, likely due to modulation by large-scale circulation anomalies and land–atmosphere feedbacks [34,44,45,46]. Such findings indicate that different heatwave metrics capture complementary aspects of extreme temperature behavior, emphasizing the value of a multi-dimensional approach.

Notably, Chinese heatwave co-occurrence networks underwent a major topological transition in the 1990s. Following this shift, NHW networks show desynchronization, with declining degree centrality and fragmented clustering, indicating that although heatwave frequency increased under global warming, events became more locally fragmented, driven by local feedbacks, urbanization, and uneven warming [47]. In contrast, NHWD networks show strengthened spatial coherence during 1994–2022, with core high-value regions expanding inland. This coexistence of desynchronized frequency and highly synchronized duration suggests that heatwaves are becoming more random in occurrence but persistent and cross-regionally coherent once initiated.

Finally, the dual-layer network analysis reveals cross-temporal and cross-spatial coupling between daytime and nighttime heatwaves. The Yangtze River Basin is strongly influenced by local processes, whereas northern China is mainly regulated by large-scale climate systems. After the abrupt transition, cross-regional co-occurrence and local clustering in northern China—particularly Northeast China—were markedly enhanced. This indicates that the propagation and persistence of heatwaves are shaped by both regional and remote mechanisms, emphasizing the complexity of heatwave dynamics under a warming climate [45].

Despite providing new insights into the spatiotemporal organization of heatwaves in China, several limitations should be acknowledged. First, this study relies on air temperature alone, which cannot fully represent heat stress, as physiological impacts depend on humidity and local climatic conditions. Future work could incorporate temperature–humidity-based indices to better assess heat-related risks. Second, although NHWD and NHW effectively characterize heatwave frequency and persistence, they do not explicitly quantify event severity; intensity metrics based on temperature exceedance above thresholds would enable a more complete evaluation of heatwave impacts. Third, the complex network metrics employed here describe statistical co-occurrence patterns among regions rather than direct physical interactions or causal mechanisms. While detrending reduces the influence of long-term warming, spatial autocorrelation and large-scale climate coherence may still contribute to network connectivity. Future studies should integrate network analysis with physical climate diagnostics—such as circulation regime variability, land–atmosphere feedbacks, soil moisture memory, and cloud–radiation processes—to better elucidate the drivers of regional heatwave synchronization and daytime–nighttime coupling.

Overall, this study advances our understanding of the spatial organization, temporal evolution, and cross-layer coupling of heatwaves in China. By contextualizing our results within global and regional studies, we highlight both shared patterns and region-specific behaviors, emphasizing the mechanistic links between local topography, land–atmosphere feedbacks, and large-scale climate variability. Such knowledge is critical for improving predictive models and informing targeted heatwave mitigation and adaptation measures.