A Study on Super-Low-Energy Building Design Strategies Based on the Quantification of Passive Climate Adaptation Mechanisms

Abstract

In response to the urgent need for developing super-low-energy buildings (SLEBs) under extreme climatic conditions, a critical research gap lies in scientifically quantifying the passive climate adaptation mechanisms of vernacular architecture and translating them into modern design strategies. To this end, this study proposes a multidimensional “Monitoring–Visualization–Quantification” analytical method. Using the Aijing Zhuang building in central Fujian, China, as a case study, this method systematically analyzed its passive regulatory performance through high-frequency monitoring and spatial-interpolation techniques. This research revealed a distinct “Gradient-Buffering-and-Dynamic-Adjustment” mechanism: a maximum indoor–outdoor temperature difference of 5.7 °C was achieved, with indoor temperature variability reduced by 62%. The courtyard, functioning as a “Thermal Buffer” and “Ventilation Hub”, orchestrated the internal climatic gradients. This study provides systematic quantitative evidence for the modern translation of traditional wisdom, and the revealed mechanism can be directly transformed into design strategies for SLEBs adapted to extreme climates.

1. Introduction

The frequent occurrence of extreme weather events, such as intense heat and high humidity triggered by global climate change, poses unprecedented challenges to building sustainability and indoor thermal comfort [1]. In this context, developing super-low-energy buildings (SLEBs) as a strategic solution to the energy crisis and climate change has become a central research focus in the international architectural community. Initiatives like Singapore’s “Super Low Energy Programme” [2] and the EU’s “Nearly Zero-Energy Buildings Strategy” [3] represent mainstream technological pathways that aim for ultimate energy efficiency through high-performance envelopes, renewable energy, and smart controls. However, the current insufficient thermal performance of envelope components often necessitates deep retrofits to enhance energy efficiency and meet nearly-zero-energy building (NZEB) targets [4]. In extreme climate zones, these solutions frequently face bottlenecks, such as high costs, complex operation and maintenance, and poor technical adaptability [5,6]. Consequently, exploring alternative solutions based on passive design principles that are cost-effective and highly resilient has emerged as a cutting-edge focus for both academia and engineering practice.

Traditional vernacular architecture, as a crystallization of human wisdom in long-term adaptation to specific climatic environments, offers invaluable inspiration through its passive design strategies [7]. These buildings create comfortable and livable indoor environments through material selection, spatial organization, construction design, and incorporating principles aligned with biophilic design without modern energy support [8]. In recent years, research utilizing modern monitoring and simulation technologies to quantify the environmental performance of vernacular buildings has increased [9,10]. Particularly in hot–humid regions, field measurements have revealed the ventilation and cooling effects of spaces like courtyards and atria [11]. The Zhuangzhai buildings in central Fujian, China, are outstanding examples. Their massive rammed-earth walls, ingenious courtyard layouts, and well-defined spatial sequences form an integrated solution for hot–humid climates [12]. However, existing studies often focus on isolated analyses of single spaces or physical phenomena, lacking a comprehensive quantitative examination of the building as a whole—a complex “Gradient-Buffering-and-Dynamic-Adjustment” system [13]. The absence of this core conceptual framework results in a lack of deep, quantifiable theoretical dialogue between traditional wisdom and modern super-low-energy design principles [14], making it difficult to formulate systematic pathways for design translation.

To address these limitations, this study aims to answer three key questions: The first, how can a multidimensional analytical method be constructed to systematically quantify the overall (rather than partial) passive climate regulatory performance of a Zhuangzhai building? Second, what are the specific physical processes and quantified efficacies of its internal “Gradient-Buffering-and-Dynamic-Adjustment” mechanism? The third, how can this traditional mechanism be translated into explicit design strategies applicable to contemporary super-low-energy buildings in extreme climates? This study hypothesizes that Zhuangzhai buildings, through the synergistic interaction of materials, spaces, and construction, form an effective dynamic-thermal-buffer system, the performance of which can be accurately revealed through integrated monitoring and spatial quantification methods.

The structure of this paper is as follows: First, by constructing a “Monitoring–Visualization–Quantification” multidimensional methodological framework (corresponding to Section 2), it addresses the lack of systematic quantitative analysis. Second, applying this method to analyze the spatiotemporal gradients of the overall thermal and wind environments (corresponding to Section 3) elucidates the “Gradient-Buffering-and-Dynamic-Adjustment” mechanism, thereby bridging the theoretical dialogue between traditional wisdom and modern principles. Finally, based on quantitative evidence, directly translatable design strategies are distilled (corresponding to Section 4 and Section 5), clarifying the modern translation pathway for vernacular strategies. This study not only provides an innovative systematic quantification framework for vernacular building performance but also offers a new paradigm for developing low-carbon building technologies adapted to extreme climates through the revealed mechanism.

2. Methodology

2.1. Study Object and Case Representativeness

This study selects the Aijing Zhuang, a well-preserved representative vernacular building in central Fujian, as the research object. Constructed during the Qing Dynasty, its symmetrical layout along a central axis, spatial organization centered around courtyards and main halls, and perimeter of massive rammed-earth walls collectively embody the typical passive design wisdom of central-Fujian Zhuangzhai buildings in response to hot–humid climates, as shown in Figure 1. The rationale for selecting the Aijing Manor as a single in-depth case study lies in its prototypical nature. According to authoritative vernacular architectural research and survey data, the Aijing Zhuang is considered exemplary of its typology in terms of scale (large-scale defensive residential complex), spatial sequence (clear “High Walls–Courtyard–Hall–Rooms” gradient), core construction (original rammed-earth walls and timber frame), and preservation state (undisturbed by modern air conditioning systems). Therefore, a deep analysis of its mechanisms can reveal climate adaptation principles common to similar buildings, providing a replicable model for subsequent research. Key technical details of the case study object are presented in

Table 1. Key technical details of the Aijing Zhuang.

Item	Details
Geographical location	It is located in Yongtai County, Fujian Province.
Construction time	Qing Dynasty.
Building area	About 3200 square meters.
Main structural materials	Peripheral load-bearing wall: rammed-earth construction. Internal structure: wooden frame.
Maintained structural features	Thick rammed-earth wall, double-slope tile roof, and transparent wooden doors and windows.
Spatial layout	Symmetrical axis and inward-facing courtyard with a clear gradient of “Public–Private” space.
Recent renovation history	Since the 2010s, protective repairs (structural reinforcement and tile surface restoration) have been carried out without changing the original materials and spatial layout.
Heating/cooling method	No centralized or split-type active heating and air conditioning system. Relies on natural ventilation and passive thermal regulation.
Energy consumption data	Due to its role as a protective building and lack of active systems, there is no energy consumption data in the traditional sense. The aim of this study is to quantify the potential for reducing theoretical energy consumption through passive regulation.

. This study acknowledges the inherent limitations of a single case regarding generalizability, which are discussed in Section 4.

2.2. Monitoring Scheme and Data Acquisition

2.2.1. Measurement Point Layout and Sensor Parameters

To comprehensively capture the climatic interaction inside and outside the building and ensure scientific rigor and representativeness, 6 synchronized measuring points were installed based on functional attributes and physical locations. These include 1 outdoor reference point and 5 functional space points, as shown in Figure 2. Point A was in an open area in front of the building (representing Outdoor climate), Point B in the Courtyard, Point C in the Hall, Point D in a Bedroom, Point E in a Dining room, and Point F in a Kitchen. Monitoring utilized 6 Kestrel 5500 handheld weather stations, which were manufactured by Nielsen-Kellerman Corporation (Located in Boothwyn, PA, USA), with these key parameters: temperature accuracy ±0.5 °C, relative-humidity accuracy ±2%, and wind-speed accuracy ±3%. All sensors were fixed at a height of 1.5 m above ground (human activity zone). To minimize human interference, all devices were mounted on tripods throughout the monitoring period and were only handled for initial setup and final retrieval.

2.2.2. Monitoring Schedule and Synchronization

Field monitoring was conducted on 21 June 2025 (summer solstice) at the Aijing Zhuang. The monitoring period covered the critical daytime period of thermal environment variation from 10:00 to 17:00. This date was chosen because, according to climatic data for central Fujian, the period around the summer solstice typically represents typical summer conditions of high temperature and solar radiation, ideal for observing the cooling pressure and efficacy of passive design. This study acknowledges the limitation of single-day measurements in reflecting seasonal variations, discussed in Section 4 as a research constraint. Prior to monitoring, all 6 Kestrel 5500 devices were placed side-by-side at outdoor Point A for 15 min to synchronize baseline readings. The data logging interval was set to 5 min. Excluding data affected by setup and retrieval, 7 h of continuous high-frequency valid time-series data were obtained.

2.2.3. Building Occupancy and Control

During monitoring, the Aijing Zhuang was unoccupied and closed to tourists. All doors and windows remained in their usual state (i.e., maintaining the building’s inherent ventilation conditions). This arrangement aimed to control confounding variables, such as internal heat gains and mechanical ventilation, allowing for a purer observation of the building envelope and spatial layout’s thermophysical response.

2.3. Data Analysis Methods

2.3.1. Time-Series Analysis and Statistical Distribution Test

Time-series analysis was employed to calculate the daily fluctuation range of temperature at each point and its phase difference with outdoor temperature. The Kolmogorov–Smirnov (K–S) test [15] was used to compare the differences in probability distributions of indoor and outdoor temperatures, quantifying the improvement in indoor environmental stability.

2.3.2. Spatial Interpolation and Thermal Environment Visualization

Based on the monitoring data, two-dimensional temperature and wind-speed field contour maps on the building section were constructed using the inverse-distance-weighting (IDW) spatial-interpolation technique. This method estimates values at unknown locations based on known point values. The calculation can be expressed in Equation (1):

$$T(x,y)={\displaystyle \frac{{\sum }_{i=1}^n\frac{T_i}{d_i^p}}{\sum _{i=1}^n\frac{1}{d_i^p}}}$$

(1)

where $\mathrm{T}\left(x,y\right)$ is the estimated temperature (or wind speed) at the interpolation point $\left(x,y\right)$, $T_i$ is the measured value at the $i$ monitoring point, $d_i$ is the distance from the interpolation point to the $i$ monitoring point, $n$ is the number of monitoring points used for interpolation, and $p$ is the power parameter (set to $p=2$ in this study). This method visually presents the temperature and wind-speed gradients within the building.

2.3.3. Quantification of Thermal Buffering Efficiency and Ventilation Efficacy

To quantify the “Thermal Buffering” function of indoor spaces (e.g., Courtyard), the thermal buffering efficiency ${\eta }_{buffer}$ is defined in Equation (2):

$${\eta }_{buffer}={\displaystyle \frac{A_{out}-A_{in}}{A_{out}}}\times 100\%$$

(2)

where $A_{out}$ and $A_{in}$ represent the temperature fluctuation amplitudes during the monitoring period for the outdoor and a specific indoor space (e.g., Courtyard), respectively. A larger value indicates a stronger attenuation effect of that space on outdoor temperature fluctuations.

3. Results

3.1. Comparison of Indoor and Outdoor Thermal Environmental Performance

Monitoring data revealed that the Aijing Zhuang exhibited excellent thermal insulation and stability performance on a typical summer day. The maximum indoor–outdoor temperature difference reached 5.7 °C (occurring around 14:00). More notably, there were significant temporal differences in the occurrence of peak temperatures across different spaces, as shown in

Table 2. Statistical results of temperature extreme value of each measuring point.

Measuring Point	Maximum Temperature (°C)	Maximum Temperature Time	Minimum Temperature (°C)	Minimum Temperature Time	Temperature Difference (°C)	25% Percentile (°C)	Median (°C)	75% Percentile (°C)	Temperature Range
Outdoor	34.4	14:10	28.9	10:45	5.5	30.6	32	32.8	28.9–34.4
Courtyard	32.8	15:15	29.2	10:00	3.6	30.6	30.9	31.2	29.2–32.8
Hall	31.4	15:40	28.2	10:00	3.2	30.2	30.9	31.2	28.2–31.4
Bedroom	31.3	16:30	27.1	10:10	4.2	28.1	29.7	30.7	27.1–31.3
Dining room	31.3	15:50	27.8	10:30	3.5	29.3	30.3	30.6	27.8–31.3
Kitchen	29.4	16:15	27.3	10:00	2.1	27.8	28.3	28.9	27.3–29.4

. The outdoor peak temperature occurred at 14:10, while the indoor peak (e.g., Bedroom) lagged until 16:30, demonstrating the thermal delay effect of the building envelope. Furthermore, the daily temperature fluctuation amplitude in an indoor space (e.g., Kitchen) was only 2.1 °C, compared to 5.5 °C outdoors, representing a 62% reduction in variability.

The time-series graph of measured temperatures on the summer solstice is shown in Figure 3. The temperature curves for the Hall and Bedroom are significantly smoother than the Outdoor curve, reflecting the superior thermal inertia of the traditional manor. This indicates better thermal stability in these spaces, effectively resisting outdoor temperature fluctuations.

The histogram of temperature distribution for indoor spaces versus outdoors on the summer solstice clearly reveals the differentiated thermal environmental characteristics of various spaces within the traditional manor, as shown in Figure 4. The temperature distributions at the six measurement points show distinct gradient differences. The outdoor temperature distribution is the most dispersed, while indoor spaces exhibit varying degrees of thermal stability, demonstrating the effectiveness of the building’s passive regulatory mechanisms.

Specifically, the outdoor temperature has the widest range (range of 5.5 °C) and a relatively flat distribution shape, reflecting the intense fluctuations in solar radiation and meteorological conditions during summer. The Courtyard, as a semi-outdoor transitional space, has a temperature distribution intermediate between outdoors and fully indoor spaces. However, its distribution curve is significantly steeper than outdoors, indicating that this space already possesses a certain thermal buffering capacity.

3.2. K–S Test: Statistical Verification of Distribution Differences and Passive Regulatory Efficacy

To move beyond simple mean comparisons and verify the fundamental difference in dynamic stability between indoor and outdoor thermal environments, a two-sample Kolmogorov–Smirnov (K–S) test was conducted. This test compares cumulative distribution functions (CDFs) and is sensitive to any differences in distribution shape, dispersion, and central tendency [16], making it ideal for assessing the “Peak-Shaving-and-Valley-Filling” effect buildings have on typically wider, flatter outdoor temperature distributions.

The results (

Table 3. Results of K–S test for temperature distribution differences between indoor spaces and outdoors.

Indoor Space	Indoor Mean ± SD (°C)	Outdoor Mean ± SD (°C)	Mean Difference (°C)	K–S Statistic	p Value	Significance
Courtyard	30.85 ± 0.69	31.78 ± 1.35	−0.93	0.4824	0	***
Hall	30.56 ± 0.81	31.78 ± 1.35	−1.22	0.5882	0	***
Bedroom	29.44 ± 1.38	31.78 ± 1.35	−2.34	0.6235	0	***
Dining room	29.91 ± 0.95	31.78 ± 1.35	−1.87	0.6235	0	***
Kitchen	28.37 ± 0.62	31.78 ± 1.35	−3.41	0.9647	0	***

*** Indicated the result is significant at a significance level of 0.001, with a confidence level of 99.9%.

) show that the temperature distributions of all indoor spaces are significantly different from outdoors ($p<0.001$). Deep spaces like bedrooms and kitchens show the largest K–S statistics (e.g., Kitchen $Dn=0.9647$), indicating the most concentrated distributions and lowest similarity to outdoors. The Courtyard, as a transitional space, has a relatively smaller K–S statistics ($Dn=0.4824$), confirming its semi-outdoor buffering characteristic. This statistically confirms a significant “Re-Shaping” of the thermal environment within the building, distinct from the raw climate.

Attributing the K–S test results to passive design is based on the following logic: First, controlled variables: the building was unoccupied and without active air conditioning during monitoring (Section 2.2.3), eliminating interference from internal heat gains and mechanical systems. Second, spatial consistency: The distribution differences revealed by the K–S test follow a clear spatial gradient (increasing significance from Outdoors → Courtyard → Hall → Rooms), perfectly matching the physical spatial sequence and envelope hierarchy. Third, physical explanation: The more concentrated, less variable indoor temperature distributions can be directly explained by passive design principles like massive rammed-earth walls (high thermal inertia) and courtyard-driven stack ventilation (continuous heat removal), which collectively filter high-frequency outdoor fluctuations.

We acknowledge that meteorological randomness (e.g., specific wind speed and cloud cover on that day) may affect absolute temperature values. However, the systematic, gradient nature of the indoor–outdoor distribution differences revealed by the K–S test is more likely dominated by the relatively stable, continuously acting physical properties of the building (i.e., passive design) rather than transient weather variations. The limitations of this attribution are further discussed in Section 4.

3.3. Internal Spatial Gradients and Synergistic Mechanisms

Spatial interpolation of discrete measurement point data clearly revealed the environmental gradients within the Aijing Zhuang. The highest temperature zone was located in the Courtyard (30.8 °C), transitioning through the Hall (30.6 °C) to the Bedroom (29.4 °C) and Kitchen (28.4 °C), forming two distinct “Low-Temperature Cores”, as shown in Figure 5.

Simultaneously, the wind-speed gradient presented a complementary pattern, as shown in Figure 6. The Courtyard, as the primary air inlet, had the highest wind speed (0.54 m/s). The airflow gradually attenuated as it permeated towards the Hall and Bedroom, forming a defined “Wind Corridor”.

The synergistic analysis in Figure 7 further shows that wind-speed isolines (white) and temperature isolines are closely coupled along the “Courtyard–Hall–Bedroom” path, indicating that thermal buoyancy (stack effect) is the main driver of airflow organization. Although the kitchen area has the lowest temperature, the sparse wind-speed isolines around it suggest that heat dissipation might be achieved through envelope insulation and local openings, forming a relatively independent microclimate unit.

These gradients are not accidental but a direct result of the synergistic effect of passive design strategies. First, the temperature gradient is primarily driven by differences in spatial function and envelope structure. Second, the wind-speed gradient is guided by the spatial sequence and openings. More crucially, the temperature and wind fields are highly coupled and synergistic in space. On one hand, the wind field enhances the homogeneity of the temperature field; airflow continuously transports heat from the Courtyard to the interior while bringing “Cooling Capacity” back from the Kitchen and Bedroom, moderating regional temperature differences. On the other hand, the temperature gradient, in turn, maintains the stability of the wind field. The stable temperature difference formed between the Courtyard and the indoor low-temperature zone acts like a natural “Heat Pump”, continuously providing power for low-speed but effective natural ventilation. This synergistic mechanism of “regulating heat with wind and promoting wind with heat” makes the building a self-regulating organic whole, achieving global optimization of the thermal environment with minimal energy consumption.

3.4. Combined Effects of Materials and Layout

Quantitative analysis of thermal buffering efficiency indicates a significant synergistic effect between the thermal inertia of materials and the sequential spatial layout (

Table 4. Quantitative results of thermal buffer efficiency (time delay and attenuation coefficient).

Indoor Space	Outdoor Peak Time	Outdoor Peak Temperature (°C)	Indoor Peak Time	Indoor Peak Temperature (°C)	$\mathbf{Time}\mathbf{Delay}{∆}_{\mathit{t}}$ (hour)	Outdoor Daily Range (°C)	Indoor Daily Range (°C)	Decay Rate (%)
Courtyard	14:10	34.4	15:15	32.8	1.1	5.5	3.6	34.5
Hall	14:10	34.4	15:40	31.4	1.5	5.5	3.2	41.8
Bedroom	14:10	34.4	16:30	31.3	2.3	5.5	4.2	23.6
Dining room	14:10	34.4	15:50	31.3	1.7	5.5	3.5	36.4
Kitchen	14:10	34.4	16:15	29.4	2.1	5.5	2.1	61.8

). On one hand, the bedroom, benefiting from its heavy peripheral envelope, exhibited the strongest thermal delay characteristic (${∆}_t$ = 2.3 h), validating that materials are the “Static Foundation” of thermal buffering. On the other hand, comparing the peak time and attenuation rate between the Courtyard and the Bedroom reveals that along the spatial sequence “Courtyard–Hall–Bedroom”, the time delay increases stepwise, while temperature fluctuation attenuates stepwise. This unveils the “Dynamic Adjustment” role of the spatial layout: the Courtyard, as the initiation zone for thermal buoyancy ventilation, first absorbs and delays part of the heat. As the airflow moves towards the bedroom, the heat it carries is continuously absorbed and further delayed by the thermal mass (walls, floors, etc.) along the path, ultimately resulting in the longest-delayed and most stable thermal environment in the bedroom. This precise coupling of “Material Heat Storage and Release” and “Spatial Airflow Organization” across temporal and spatial dimensions is the intrinsic physical mechanism enabling the “Gradient-Buffering-and-Dynamic-Adjustment” microclimate. The results—including time-series phase lag, K–S test-confirmed distribution concentration, and spatial gradient visualization—collectively form a robust chain of quantitative evidence supporting this mechanism.

4. Discussion

4.1. Dialogue with Existing Research: From Qualitative Description to Systematic Quantification

Existing research widely acknowledges the climate adaptability of vernacular architecture, but most of them focus on the qualitative induction of spatial types [17,18] or the simulation analysis of single physical performances (e.g., courtyard ventilation) [19,20]. A few field monitoring studies, such as summer thermal environment tests of southern Chinese dwellings [21], provide empirical data, but their analysis is often limited to indoor–outdoor temperature comparisons, failing to reveal the systemic mechanism of the collaborative work of various spaces within the building. By employing the multidimensional “Monitoring–Visualization–Quantification” method, this study not only verifies the significant insulation performance of the Aijing Zhuang (maximum indoor–outdoor temperature difference of 5.7 °C) but, more importantly, for the first time, quantifies and reveals the gradient coupling relationship of its internal “Temperature–Wind-Speed” fields through spatial interpolation and distribution tests. This partially aligns with findings from Hao et al. [22] on summer ventilation paths in a siheyuan, but our study further quantifies the thermal attenuation efficiency along this path and clarifies the courtyard’s dual-coupled function as a “Thermal Buffer” and “Ventilation Hub,” thereby advancing traditional understanding from “Functional Description” to “Efficacy and Mechanism Quantification”.

4.2. Systematic Analysis and Attribution of the “Gradient-Buffering-and-Dynamic-Adjustment” Mechanism

The results of this study show that the excellent thermal stability of the Aijing Zhuang (the indoor temperature fluctuation is reduced by 62%) is due to the “Gradient-Buffering-and-Dynamic-Adjustment” mechanism composed of space, materials, and airflow. This mechanism employs a multi-layered spatial sequence from outside to inside, from open to enclosed (i.e., outdoor environment → shaded area by high walls → courtyard → hall → rooms) to progressively attenuate the extremity of the external climate (e.g., high temperature and strong radiation). The K–S test confirmed that there was a statistically significant difference in the distribution of indoor and outdoor thermal environments ($p<0.001$), and the degree of difference increased with the deepening of space. This gradient distribution difference model strongly supports that the dominant cause is the stable and continuous physical properties of the building itself (such as high thermal inertia rammed-earth walls and fixed spatial sequences), rather than the instantaneous meteorological fluctuations that may occur on the monitoring day. Of course, we acknowledge that monitoring based on a typical single-day meteorological day can effectively capture the working principle of the mechanism under high load, but it cannot fully reflect its performance in spring and autumn or different weather patterns. At the same time, the in-depth analysis of a single representative case, while fully revealing its internal mechanism, also means that the research conclusions need to be cautious when extended to other types of vernacular architecture. These are the trade-offs made by this study in the pursuit of in-depth mechanism analysis and are also the directions that can be expanded in future research.

4.3. Strategic Implications and Translation Pathways for Contemporary Super-Low-Energy Building Design

The quantitative evidence from this study provides translatable pathways for modern design that go beyond metaphor and are actionable. First, it advocates transforming the concept of “Climate Buffer Space” from a vague notion into a calculable, performance-based design element. For instance, in contemporary architecture, the atrium or wind tower can be estimated and optimized using the quantitative method for the thermal buffering efficiency (${\eta }_{buffer}$) of the atrium in this study. Second, it supports a shift from homogeneous environmental control to gradient-based environmental zoning. This aligns with the “Adaptive Thermal Comfort” theory [23], allowing for greater environmental fluctuations in non-core zones, thereby significantly reducing energy demand. Finally, this study emphasizes the integrated synergy of materials and space. For example, placing phase-change materials or activated concrete systems at key nodes in the ventilation path to reproduce the dynamic effect of traditional rammed-earth walls’ “Breathing Temperature Regulation”. This translation is not a formal imitation but an inheritance and innovation of the traditional wisdom’s systematic, layered thinking mode of climate response in traditional wisdom.

5. Conclusions

5.1. Main Research Findings

Through empirical monitoring and quantitative analysis of the Aijing Zhuang in central Fujian, this study systematically analyzed the passive climate adaptation mechanism of vernacular architecture, yielding the following core findings: First, on a typical summer day, the Zhuangzhai buildings effectively attenuate outdoor thermal shock, with a maximum indoor–outdoor temperature difference of 5.7 °C and a significant 62% reduction in indoor temperature variability compared to outdoors. Second, a clear synergistic “Temperature–Wind-Speed” gradient forms within the building, revealing its physical essence as the “Gradient-Buffering-and-Dynamic-Adjustment” mechanism. Third, the courtyard is confirmed as the core hub driving this mechanism, possessing the dual coupled functions of the “Thermal Buffering” and “Ventilation Hub”. Fourth, the synergy between the high thermal inertia of traditional materials and the gradient spatial layout is the foundation enabling this efficient, low-energy regulation.

5.2. Research Contributions and Significance

The contribution of this study lies in achieving a leap from qualitative cognition to quantitative analysis, and from observing partial phenomena to understanding systemic mechanisms. On a theoretical level, the constructed “Monitoring–Visualization–Quantification” methodological system provides a reproducible analytical framework for traditional building performance research. The extracted concept model of “Gradient Buffering and Dynamic Adjustment” offers a new perspective for understanding the climate adaptability of complex building systems. On a practical level, the research conclusions provide direct strategic translation pathways for contemporary ultra-low-energy building design—namely, shifting from a “Confrontational” design reliant on high-performance equipment to a “Guiding” passive design that utilizes the synergy between space and materials. This holds important reference value for developing low-carbon building technologies adapted to extreme climates.

5.3. Research Limitations and Future Prospects

This study also has several limitations that should be considered when interpreting and applying its conclusions: First, the in-depth analysis based on a single typical case implies that its conclusions require cautious verification when extended to other types of buildings. Second, although monitoring during a single typical meteorological day effectively captures the mechanistic principle, it cannot comprehensively reflect seasonal performance. Furthermore, while major variables were controlled and systematic distribution differences were attributed to passive design in the analysis, the potential influence of meteorological random factors on absolute measured values remains. Future research can delve into the following directions: extending monitoring to different seasons to obtain annual performance profiles; conducting comparative studies with multiple cases to verify the mechanism’s universality; and developing performance-based design tools that integrate traditional wisdom, based on the quantitative models from this study.