Linking Urban Heat Island Intensification with Rainfall Variability in Tropical Archipelagic Indonesia

Linking Urban Heat Island Intensification with Rainfall Variability in Tropical Archipelagic Indonesia | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Linking Urban Heat Island Intensification with Rainfall Variability in Tropical Archipelagic Indonesia Deasy Mukti, Halmar Halide This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8952979/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Urbanization in humid tropical archipelagic environments alters surface energy balance and atmospheric moisture dynamics, yet the coupled behavior of Urban Heat Island (UHI) intensity and rainfall extremes in such settings remains poorly quantified. This study investigates the interactions between urban thermal amplification and precipitation variability using quality-controlled daily observations (2009–2025) from three BMKG stations in South Sulawesi, Indonesia, representing an urban–airport site (Hasanuddin), a coastal urban site (Paotere–Makassar), and a rural inland reference (Masamba). While no statistically significant long-term trend is detected in mean rainfall across stations, extreme precipitation exhibits pronounced spatial differentiation. Generalized Extreme Value modeling reveals that the 50-year return level at the urban–airport site reaches approximately 330 mm, substantially exceeding the ~210 mm estimated at the rural station, indicating urban-enhanced rainfall extremes. Concurrently, a persistent positive UHI signal (+1.4 to +1.8 °C in annual mean temperature) is observed at the urban–airport site relative to the rural reference, whereas the coastal urban station shows near-neutral or slightly negative anomalies consistent with sea-breeze moderation and high moisture availability. Daily-scale analyses demonstrate robust negative associations between UHI intensity and both rainfall and relative humidity (Spearman ρ ≈ –0.54 to –0.66, p < 0.001), suggesting that enhanced latent heat fluxes and cloud-related radiative effects suppress urban thermal contrast under wet conditions. These results indicate the coexistence of urban-amplified rainfall extremes (“rain island”) and a moisture-suppressed UHI regime, highlighting nonlinear feedbacks between thermal forcing and hydrological processes in tropical coastal cities. By providing one of the first integrated observational assessments of UHI–rainfall coupling in an archipelagic Southeast Asian context, this study advances understanding of urban hydroclimatic feedbacks and underscores the importance of incorporating moisture–thermal interactions into climate adaptation strategies, urban design, and flood-risk management in rapidly urbanizing tropical regions. Urban Heat Island rainfall extremes tropical coastal climate Generalized Extreme Value distribution sea-breeze modulation Indonesia Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1 Introduction Climate change is a multidimensional global challenge that disrupts hydrometeorological systems, ecosystems, and socio-economic stability worldwide (IPCC 2021 ). In tropical developing nations such as Indonesia, these impacts are intensified by rapid urbanization and land-use change, which alter surface energy exchanges and local climatic balances (Zhou et al. 2022 ; Winsemius et al. 2016 ). Yet, despite extensive research on rainfall variability and the Urban Heat Island (UHI) effect individually, their combined dynamics remain underexplored in tropical archipelagic contexts. This study advances the field by providing one of the first integrated assessments of rainfall–UHI interactions in Indonesia, highlighting how urbanization-driven warming influences local rainfall regimes. By focusing on South Sulawesi, we demonstrate the novelty of linking urban climate modification with hydrometeorological variability in a region where both processes critically shape resilience, water security, and public health (Ward et al. 2020 ; Clarke et al. 2023 ). Rainfall variability in the tropics often manifests through shifts in monsoon onset, rainfall intensity, and extreme precipitation frequency (Blöschl et al. 2019 ; Nicholls et al. 2008 ). Indonesia’s coastal and archipelagic environments are especially vulnerable due to ocean–atmosphere interactions and complex topography (Shen et al. 2023 ). Meanwhile, UHI intensification—driven by impervious surfaces and vegetation loss—has raised local temperatures, altered humidity, and reshaped convective rainfall dynamics (Zhou et al. 2022 ; Ward et al. 2020 ). These processes represent a compound interaction of global climate drivers and local anthropogenic forcing. Recent studies across Asia highlight reciprocal linkages between UHI and rainfall variability, including the “rain island” phenomenon where urban warming enhances localized convection and precipitation (Zhang et al. 2019 ; Li et al. 2021 ). Evidence from China and India shows urbanization reshaping rainfall distribution and amplifying hydrometeorological risks during monsoon seasons (Wang et al. 2017 ; Sati et al. 2024 ). Such findings underscore the need for integrated approaches that combine meteorological, spatial, and socio-economic perspectives in tropical developing contexts. South Sulawesi, Indonesia, offers a compelling case to examine these dynamics. The province’s monsoonal rainfall, rapid coastal urbanization, and diverse topography (Ward et al. 2020 ) intersect with economic sectors highly dependent on climatic stability (Clarke et al. 2023 ). Rainfall fluctuations challenge agriculture, while rising urban temperatures exacerbate energy demand, heat-related illness, and reduced labor efficiency (Gupta et al. 2021 ). Understanding rainfall–UHI interactions is therefore vital for both scientific insight and practical adaptation in water management, land-use planning, and sustainable development. Despite growing attention, few studies quantitatively assess rainfall and UHI together in tropical archipelagic regions (Clarke et al. 2023 ; Shen et al. 2023 ). Most analyze precipitation or temperature trends separately, leaving their mutual dynamics underexplored. This study addresses that gap by jointly examining rainfall and temperature records from three BMKG stations in South Sulawesi—Hasanuddin (urban–airport), Paotere/Makassar (coastal urban), and Andi Jemma/Masamba (rural inland). Using Mann–Kendall tests, Sen’s slope estimation, and Generalized Extreme Value (GEV) modeling, we provide one of the first integrated empirical assessments of rainfall–UHI interactions in Indonesia’s tropical archipelagic setting. By explicitly linking urbanization-driven warming with rainfall variability, this research contributes novel evidence to strengthen regional climate resilience frameworks and inform adaptation strategies across rapidly developing tropical regions. This paper is structured as follows. Section 2 outlines the methodological framework, including a description of the study area and data sources, the data preprocessing steps and statistical overview, inter-station comparison and trend detection procedures, as well as the analytical approaches used to examine temperature, Urban Heat Island (UHI) characteristics, and broader climate variability. Section 3 presents the results and discussion, covering rainfall extremes and the application of the Generalized Extreme Value (GEV) distribution, return period estimation, rainfall trends and spatial variability, UHI patterns, and a combined interpretation of UHI–rainfall interactions and UHI trend behavior. Section 4 provides the conclusion of the study. 2 Methods 2.1 Study Area and Data Sources The analysis draws on daily rainfall and air temperature observations from three meteorological stations operated by the Meteorology, Climatology, and Geophysics Agency of Indonesia (BMKG) in South Sulawesi Province. These sites were selected to represent contrasting degrees of urbanization and topographic settings: Hasanuddin (HND), an urban–airport environment; Paotere/Makassar (MKS), a coastal urban zone; and Andi Jemma/Masamba (MSB), a rural inland station used as the thermal reference. The datasets span January 2009 to August 2025 and include minimum, maximum, and mean temperature variables alongside rainfall totals. All records underwent quality assurance procedures to remove missing, duplicate, or anomalous entries prior to analysis. 2.2 Data Preprocessing and Statistical Overview Data from each station were standardized into a consistent time-series structure consisting of date and measurement variables. Missing entries were eliminated using MATLAB’s rmmissing() function, and all date formats were unified via datetime conversion to ensure temporal consistency. Rainfall data were imported from rainfall09-25.xlsx , while temperature records were sourced from station-specific text files ( Coastal.xlsx, Airport.xlsx, and Remote.xlsx ). Descriptive statistics, including mean, median, standard deviation, skewness, and kurtosis, were computed to characterize the statistical behavior of rainfall. Normality was assessed using the Kolmogorov–Smirnov (KS) test to determine the suitability of parametric (t-test) or nonparametric (Mann–Whitney U) methods for inter-station comparisons. 2.3 Inter-Station Comparison and Trend Detection Pairwise comparisons were conducted to evaluate statistical differences among the three stations. Normally distributed data were tested using the t-test, while non-normal data employed the Mann–Whitney U test, with variance homogeneity evaluated via MATLAB’s vartest2(). Correlation analyses employed Pearson’s (r) and Spearman’s (ρ) coefficients to capture linear and monotonic associations, respectively, with MSB serving as the rural reference site. Long-term rainfall trends were quantified using both linear regression and the nonparametric Mann–Kendall (MK) test. The regression model \((Y={\beta}_{0}+{\beta}_{1}t+\epsilon)\) provided estimates of trend magnitude (β₁), standard error, confidence intervals, and explained variance (R²), while the MK test identified the direction and significance of monotonic changes independent of distributional assumptions. 2.4 Temperature, UHI, and Climate Variability Analysis When rainfall patterns among stations showed no significant divergence, additional analyses were undertaken to examine the Urban Heat Island (UHI) effect. UHI intensity was calculated on a daily basis as $$UHI\left(t\right)={T}_{urban}\left(t\right)-{T}_{rural}\left(t\right)$$ where the Masamba station (MSB) serves as the rural thermal reference. This formulation allows UHI variability to be evaluated at daily resolution using Tmin, Tmean, and Tmax. The analysis does not assume a unidirectional causal influence of UHI on rainfall or humidity; instead, it focuses on their coupled and immediate co-variability under humid tropical coastal conditions. Annual temperature trends for Tmin, Tmax, and Tmean were subsequently derived using linear regression models of the form where the slope ( \(\beta\) ) represents the annual rate of change (°C yr⁻¹). Identical procedures were applied to estimate temporal trends in UHI intensity. All analyses were performed in MATLAB R2023b (Windows 10) using functions fitlm() , corr() , and kstest() , supplemented by custom scripts for Mann–Kendall and UHI computations. Statistical significance was evaluated at the 95% confidence level (α = 0.05). Outputs were provided in tabular and graphical formats—including rainfall_trend_results.csv and Tren_Suhu_UHI.xlsx —with visualizations comprising time-series plots, regression lines, and 95% confidence intervals. 3 Results and Discussion 3.1 Annual Maximum Rainfall and GEV Modeling The annual maximum daily rainfall series (2009–2025) were fitted to the Generalized Extreme Value (GEV) distribution (Fig. 2 a–c). The urban–airport station (Hasanuddin) exhibits the heaviest upper tail, with several events exceeding 250 mm and a clear right-skewed distribution. The coastal urban station (Makassar) shows moderate extremes (mostly 120–180 mm), while the rural inland station (Masamba) displays the lowest magnitudes and least dispersion (modal value 100–120 mm). The rainfall extremes at the coastal MKS station (Fig. 2 b) exhibit a somewhat narrower distribution, with most values ranging between 120 and 180 mm. The fitted GEV curve indicates a predominance of moderate-intensity extremes, reflecting Makassar’s maritime climatic setting where interactions between land–sea breezes and moist marine air masses modulate convective development (Qian et al., 2013). Compared with HND, the shorter tail at MKS suggests a lower likelihood of exceptionally high rainfall events. The MSB station (Fig. 2 c), situated inland at higher elevation, shows the lowest range of annual maximum rainfall, with a modal value of approximately 100–120 mm. The sharper and less skewed GEV profile suggests lower interannual variability and reduced occurrence of extreme rainfall. This aligns with findings that inland highland or orographic regions in Indonesia often experience more stable precipitation regimes dominated by topographic lifting rather than intense convective systems (Hidayat & Ando, 2014). 3.2 Return Level Analysis Return level plots (Fig. 2 d) confirm substantial spatial divergence. At the 50-year return period, estimated rainfall intensities are 330 mm (Hasanuddin), 300 mm (Makassar), and 210 mm (Masamba). The elevated extremes at the urban–airport site support the presence of a “rain island” effect driven by enhanced surface heating, roughness, and boundary-layer instability (Zhang et al., 2019 ; Li et al., 2021 ). Table 1 GEV parameters and selected return levels (2009–2025) Station Location (µ) Scale (σ) Shape (ξ) 10 year (mm) 50 year (mm) 100 year (mm) Hasanuddin 143.2 48.6 0.12 255 330 365 Makassar 136.8 41.3 0.08 235 300 330 Masamba 112.4 32.7 0.05 175 210 230 3.3 Long-Term Rainfall Trends Mann–Kendall tests and Sen’s slope estimates reveal no statistically significant trends in annual or seasonal rainfall totals at any station (p > 0.4; Table 2 ). Interannual variability is dominated by the Asian–Australian monsoon and ENSO/IOD phases, consistent with previous regional studies (Supari et al. 2017). The three daily rainfall series (Fig. 3 a–c) reveal a distinct set of characteristics that complement the statistical finding of no significant long-term trends. A prominent feature across all stations is the strong and repetitive seasonal cycle, with rainfall peaks occurring almost annually. This pattern reflects the dominant influence of the Austral–Asian monsoon system, where the wet season intensifies through strengthened northwesterly monsoon flow and the development of localized convergence zones. Such a consistent seasonal signature suggests that short-term monsoonal dynamics exert a stronger control over rainfall variability than any long-term climatic trend (Aldrian and Susanto 2003; Qian et al. 2013). Among the three sites, the Hasanuddin station (HND) exhibits the most pronounced variability, with sharper fluctuations and more frequent high-intensity events. This behaviour is likely linked to its low-elevation coastal setting combined with interactions within the surrounding urban–airport boundary layer, which can enhance localized convective development. Previous studies have shown that coastal urban areas often display elevated convective rainfall variability due to increased surface roughness and alterations to boundary-layer airflow (Kusumaningtyas et al. 2021; Li et al. 2020). In contrast, rainfall at the Paotere–Makassar station (MKS) demonstrates a more uniform pattern with no evident rise in extreme event magnitudes, aligning with its high Mann–Kendall p-value (p > 0.8). This stability reinforces the notion that Makassar’s coastal environment is driven predominantly by short-term land–sea interactions rather than long-term atmospheric shifts. Previous regional research similarly reports that rainfall along coastal South Sulawesi is more sensitive to diurnal sea–land breeze mechanisms than to persistent climate-scale forcing (Juneng and Tangang 2005; Pratikto et al. 2022). The Masamba station (MSB), situated at a higher elevation, records generally lower rainfall intensities, yet still displays clear annual fluctuations. This pattern is typical of upland and valley regions where rainfall production is strongly modulated by orographic lifting and local topographic effects. Studies in northern and southern Sulawesi similarly report that highland rainfall variability is primarily governed by terrain-driven processes rather than long-term climatic tendencies (Sulistyowati et al. 2020; Hidayat and Ando 2014). Taken together, the absence of statistically significant long-term rainfall trends across all stations supports the broader understanding that rainfall variability in the Indonesian Maritime Continent is largely shaped by interannual modes such as ENSO and the Indian Ocean Dipole (IOD). Numerous climatological assessments highlight that, while extreme rainfall events may evolve in response to localized atmospheric–oceanic feedbacks, the mean rainfall in this region often shows weak or insignificant trends over multi-decadal periods (Chang et al. 2005; Supari et al. 2017). Table 2 Statistical Results of Daily Rainfall Trend Analysis (2009–2025) at Three Meteorological Stations in South Sulawesi Station Trend (mm/day) (SE) p value \({R}^{2}\) (%) HND 0.000414962 0.000159079 0.009115469 0.00111698 MKS 0.000053092 0.000147056 0.718089353 0.00002142 MSB 0.000302276 0.000141958 0.033266880 0.00074456 3.4 Urban Heat Island Intensity and Trends Although no statistically significant long-term trends are detected in mean or seasonal rainfall totals (Section 3.3 ), the marked spatial divergence in extreme rainfall magnitudes—particularly the elevated return levels at the urban–airport station—motivates an examination of concurrent modifications in local temperature regimes through the Urban Heat Island (UHI) effect. Here, UHI is treated as a diagnostic indicator of urban–rural thermal contrast rather than a direct forcing mechanism, consistent with recent urban climate assessments emphasizing coupled land–atmosphere interactions under climate change (IPCC 2021 ; Zhou et al. 2022 ). UHI intensity, defined relative to the rural Masamba station, exhibits strong spatial heterogeneity across South Sulawesi (Table 3 ). The Hasanuddin urban–airport site shows a consistently positive and statistically significant UHI signal, with an annual mean intensity of + 1.58°C (Tmean) and + 2.11°C (Tmax). Moreover, the UHI trend is significantly increasing at rates of 0.042°C yr⁻¹ (p < 0.01) for Tmean and 0.055°C yr⁻¹ (p < 0.001) for Tmax, indicating a robust intensification of urban warming over time. In contrast, the coastal Makassar station displays a slightly negative mean UHI (–0.42°C), with a weak and statistically non-significant trend (–0.018°C yr⁻¹, p = 0.09), suggesting that maritime influences largely offset classical daytime urban warming. Trend analysis further reveals contrasting temporal evolutions of UHI intensity between stations. At Hasanuddin, both Tmean- and Tmax-based UHI exhibit statistically significant positive trends, reaching up to + 0.055°C yr⁻¹ over the 2009–2025 period. This gradual intensification is consistent with increasing urban–rural thermal contrast associated with continued land-surface modification. However, this signal must be interpreted in conjunction with the pronounced warming observed at the rural reference site, particularly in Tmin (R² = 0.311), which reflects broader regional climate warming affecting both urban and rural environments (IPCC 2021 ). As highlighted in previous studies, warming at rural reference stations can partially mask future UHI amplification if background climate change is not explicitly accounted for (Li et al. 2021 ; Zhou et al. 2022 ). The muted or negative UHI at the coastal Makassar station underscores the dominant role of local physical controls specific to humid tropical coastlines. Persistent sea-breeze circulation, high atmospheric moisture content, and frequent cloud cover act to limit daytime radiative heating while enhancing latent heat flux, thereby suppressing the development of a classical urban heat island (Zhang et al. 2019 ; Wang et al. 2017 ). Similar coastal UHI weakening has been documented in other maritime tropical cities, where moisture availability and advective cooling outweigh the warming effects of urban surfaces (Kim and Baik 2002 ; Zhou et al. 2022 ). Taken together, the pronounced UHI signal at the inland urban–airport site and the suppressed or absent UHI along the immediate coast delineate two distinct urban thermal regimes within the same metropolitan region. This spatial contrast provides the physical context for interpreting the daily-scale coupling between UHI intensity, rainfall, and relative humidity examined in the following section. In particular, it suggests that in South Sulawesi, UHI variability is highly sensitive to atmospheric moisture conditions and short-term hydro-meteorological variability, preconditioning the negative UHI–rainfall and UHI–humidity relationships identified at daily timescales. Scatterplot showing Coast UHI(Tmin) versus rainfall (Lag 0). A significant negative slope (β = − 0.0091, p < 0.001) demonstrates that increased rainfall weakens the urban–rural temperature contrast. Rainfall-induced evaporative cooling and cloud cover likely reduce UHI formation at the coastal site. Scatterplot showing Coast UHI(Tmin) versus relative humidity (Lag 0). The relationship is weak and statistically insignificant (β = − 0.0036, p = 0.289), suggesting that nighttime UHI at the coast is less sensitive to humidity variability compared to the airport station. Scatterplot showing the linkage between Airport UHI(Tmin) and daily rainfall (Lag 0). The regression slope is negative (β = − 0.0072, p < 0.001), indicating that higher rainfall is associated with weaker nighttime UHI intensity. The distribution shows wide variability at low rainfall but a consistent downward trend as rainfall increases. Scatterplot of Airport UHI(Tmin) versus daily relative humidity (Lag 0). The regression slope is positive (β = +0.0531, p < 0.001), indicating that nighttime UHI intensity slightly increases under higher humidity, likely due to enhanced longwave trapping and reduced nighttime cooling Table 3 Mean UHI intensity and linear trends (2009–2025) UHI definition Mean UHI (°C) Trend (°C yr⁻¹) p-value R² Hasanuddin – Masamba (Tmean) 1.58 0.042 < 0.01 0.28 Makassar – Masamba (Tmean) –0.42 –0.018 0.09 0.11 Hasanuddin – Masamba (Tmax) 2.11 0.055 < 0.001 0.34 3.5 Daily-Scale Modulation of UHI by Rainfall and Humidity At the daily scale, UHI intensity at both the airport and coastal stations exhibits a consistent and statistically significant negative relationship with rainfall and relative humidity. For Tmax- and Tmean-based UHI, regression coefficients linking UHI to rainfall and humidity are significantly negative at Lag 0 (e.g., Airport Tmax: β_Rain ≈ − 0.015, β_RH ≈ − 0.092; p < 0.001), with Spearman correlations ranging from − 0.54 to − 0.66. These relationships weaken rapidly beyond a one-day lag, indicating that the suppression of UHI is primarily an immediate response to rainfall-induced evaporative cooling, increased cloud cover, and enhanced latent heat fluxes. The coastal station exhibits the strongest negative coupling, reflecting continuous moisture advection from the adjacent sea and frequent cloudiness that constrain radiative heating. An exception is observed for Tmin-based UHI at the airport station, which shows a weak positive association with humidity. This behavior is consistent with nocturnal radiative trapping under humid conditions and reduced longwave cooling, indicating distinct controls on daytime versus nighttime UHI dynamics. 3.6 Synthesis: Rain Island alongside Moisture-Suppressed UHI The urban climate of South Sulawesi therefore exhibits two coexisting but physically consistent phenomena. First, a localized enhancement of extreme daily rainfall is detected at the urban–airport site, consistent with processes often associated with urban surface roughness, thermal perturbations, and boundary-layer modification. Second, UHI intensity is strongly constrained by moisture availability, producing a moisture-suppressed UHI regime in which wet and humid conditions limit daytime urban heating. This dual behavior reconciles contrasting findings in the literature. While numerous studies document urban-enhanced convection and rainfall under favorable thermodynamic conditions, others report urban-induced drying or suppressed convection in humid coastal or marine-influenced environments. Makassar clearly aligns with the latter category, emphasizing the context dependence of UHI–rainfall interactions in tropical coastal settings. 4 Conclussion This study provides observational evidence from a tropical archipelagic setting demonstrating that urbanization can simultaneously intensify rainfall extremes while constraining Urban Heat Island development. Although mean rainfall in South Sulawesi shows no significant long-term trend, extreme daily precipitation is substantially amplified at the urban–airport site, with 50-year return levels approximately 60% higher than those at the rural inland reference station. At the same time, UHI intensity is strongly modulated by moisture availability and coastal circulation, resulting in a modest but significant positive UHI confined to inland-urban environments and near-zero or negative anomalies along the immediate coast. The pronounced negative coupling between UHI intensity, rainfall, and relative humidity at daily time scales indicates that humid tropical coastal cities operate under a moisture-limited UHI regime, where wet conditions act as an effective brake on urban overheating. These findings reconcile apparently conflicting urban climate paradigms and underscore the necessity of region-specific interpretations of UHI–rainfall interactions. For rapidly growing coastal cities such as Makassar, climate adaptation strategies must address the compound risk of intensified rainfall extremes alongside moderated—yet still relevant—heat stress, rather than assuming continental-style UHI amplification. Implications for Urban Climate Adaptation Given the dual signature identified in this study — intensified extreme rainfall alongside a strongly moisture-suppressed Urban Heat Island in a humid tropical coastal city — the following targeted actions are recommended: Flood-risk mapping and early-warning systems for Makassar and similar archipelagic cities must be updated to reflect the observed 50–60% higher return levels of daily extreme rainfall in urban and peri-urban zones, even in the absence of significant mean rainfall trends. Current design standards based on rural or historical rural-reference statistics will substantially underestimate flood hazard. UHI mitigation strategies in coastal Sulawesi and comparable Southeast Asian cities should prioritize moisture-retentive and evaporative cooling solutions (urban parks, green roofs, blue–green corridors, water-retentive pavements) over purely reflective “cool materials”. Our results show that increasing latent heat flux is more effective at suppressing the (already modest) daytime UHI, whereas high-albedo surfaces alone may be less beneficial in persistently humid environments. Future urban expansion planning around Hasanuddin International Airport and other inland-urban zones requires explicit consideration of “rain island” amplification. New impervious developments should be coupled with large-scale retention basins and upstream reforestation to offset enhanced convective extremes. Observational networks need at least one additional high-quality rural reference station located > 30–40 km inland and at similar elevation to Masamba, but outside the expanding urban plume, to prevent underestimation of future UHI trends as the current rural sites themselves warm. Modeling and attribution studies should employ convection-permitting regional climate models with explicit urban canopy parameterization and sea-breeze representation to quantify the separate contributions of surface imperviousness, aerosol effects, and sea-breeze dynamics to the observed rainfall intensification and UHI suppression. Implementing these context-specific measures will substantially improve compound heat-flood resilience in rapidly urbanizing tropical archipelagic regions where classical continental UHI paradigms do not apply. Declarations Author Contributions Deasy Mukti (DM): Conceptualization, Methodology, Software, Formal analysis, Investigation, Data curation, Writing – original draft, Visualization. Halmar Halide (HH): critically revised the article for final approval. All authors reviewed and approved the final version of the manuscript and agree to be accountable for all aspects of the work. Conflict of Interest The authors state that there are no conflicts of interest related to this study. Funding This study was conducted without financial support from any public, commercial, or non-profit funding organizations. Coding and Data Availability Statement Both the datasets used and the MATLAB® coding used in this work are available by request to DM. Acknowledgements The authors express their gratitude to the Meteorology, Climatology, and Geophysics Agency for providing the rainfall and temperature datasets. The authors also acknowledge the use of ChatGPT (developed by OpenAI) as a supporting tool during the manuscript preparation process. Use of AI Tools ChatGPT (OpenAI) and Grok (xAI) were employed to assist with language editing, clarity enhancement, and formatting during manuscript preparation. All outputs generated with the assistance of AI tools were carefully reviewed and validated by the authors to ensure scientific accuracy and integrity. References Blöschl G, Hall J, Viglione A, Perdigão RAP, Parajka J, Merz B, Arheimer B et al (2019) Changing climate shifts timing of European floods. Science 365(6451):588–590. https://doi.org/10.1126/science.aan2506 Clarke L, Jiang K, Akimoto K, Babiker M, Blanford G, Fisher-Vanden K, Zhou N et al (2023) Climate Change 2023: Mitigation of Climate Change. Contribution of Working Group III to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press Gupta J, van der Grijp N, Kuik O (2021) Climate change, development and sustainability: A critical review. Environ Sci Policy 124:1–12. https://doi.org/10.1016/j.envsci.2021.05.002 IPCC (2021) Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press Kim YH, Baik JJ (2002) Maximum urban heat island intensity in Seoul. J Appl Meteorol 41(6):651–659. https://doi.org/10.1175/1520-0450(2002)0412.0.CO;2 Li D, Sun T, Liu M, Yang L, Wang L, Gao Z (2021) Contrasting responses of urban and rural surface energy budgets to heatwaves explain the urban heat island amplification. Nat Commun 12:2620. https://doi.org/10.1038/s41467-021-22875-0 Li H, Zhou Y, Jia G, Zhao K, Dong J (2021) Quantifying the response of surface urban heat island to urbanization using the annual temperature cycle model. Geosci Front 13(1):101141. https://doi.org/10.1016/j.gsf.2021.101141 Lin CY, Chen WC, Chang PL, Sheng YF (2011) Impact of the urban heat island effect on precipitation over a complex geographic environment in northern Taiwan. J Appl Meteorol Climatol 50(2):339–353. https://doi.org/10.1175/2010JAMC2504.1 Nicholls RJ, Hanson S, Herweijer C, Patmore N, Hallegatte S, Corfee-Morlot J, Château J, Muir-Wood R (2008) Ranking port cities with high exposure and vulnerability to climate extremes: Exposure estimates. OECD Environment Working Papers No 1. OECD Publishing. https://doi.org/10.1787/011766488208 Otto FEL, Harrington L, Schmitt K, Philip S, Kew S, van Oldenborgh GJ, Singh R, Kimutai J, Wolski P (2020) Challenges to understanding extreme weather changes in lower-income countries. Bull Am Meteorol Soc 101(10):E1851–E1860. https://doi.org/10.1175/BAMS-D-19-0317.1 Sati AP, Singh RB, Chandel V (2024) Urbanization and monsoon rainfall variability in Indian megacities: Emerging risks under climate change. Clim Dyn 63(4):1453–1470. https://doi.org/10.1007/s00382-024-06874-2 Shepherd JM (2005) A review of current investigations of urban-induced rainfall and recommendations for the future. Earth Interact 9(12):1–27. https://doi.org/10.1175/EI156.1 Shen Y, Liu J, Li J (2023) Compound flood risks in tropical regions under changing climate and urbanization: A review. Earths Future 11(5):e2023EF003020. https://doi.org/10.1029/2023EF003020 Vasconcelos Junior FdC, Zachariah M, Silva TLdV, dos Santos EP, Coelho CAS, Alves LM, Otto FEL et al (2024) An attribution study of very intense rainfall events in Eastern Northeast Brazil. Weather Clim Extrem 45:100699. https://doi.org/10.1016/j.wace.2024.100699 Wang J, Zhang M, Guo Y (2017) Effects of urbanization on precipitation over the Yangtze River Delta: Dynamic and thermodynamic mechanisms. J Geophys Res Atmos 122(13):7277–7290. https://doi.org/10.1002/2017JD026774 Ward PJ, Jongman B, Weiland FS, Bouwman A, van Beek R, Bierkens MFP, Ligtvoet W, Winsemius HC (2020) Assessing flood risk at the global scale: Model setup, results, and sensitivity. Environ Res Lett 15(3):034015. https://doi.org/10.1088/1748-9326/ab7e2a Winsemius HC, Aerts JCJH, van Beek LPH, Bierkens MFP, Bouwman A, Jongman B, Ward PJ et al (2016) Global drivers of future river flood risk. Nat Clim Change 6(4):381–385. https://doi.org/10.1038/nclimate2893 Yang H, Wu Z, Dawson RJ, Barr S, Ford A, Li Y (2024) Quantifying surface urban heat island variations and patterns: Comparison of two cities in three-stage dynamic rural–urban transition. Sustain Cities Soc 109:105538. https://doi.org/10.1016/j.scs.2024.105538 Zhang C, Chen F, Miao S (2019) Urbanization and rainfall change: Understanding the linkage through boundary-layer processes. J Clim 32(2):349–367. https://doi.org/10.1175/JCLI-D-18-0328.1 Zhou D, Zhao S, Zhang L, Liu S (2022) Remotely sensed assessment of urban heat island and its spatial-temporal dynamics: A review. Remote Sens Environ 273:112978. https://doi.org/10.1016/j.rse.2022.112978 Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8952979","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":600903840,"identity":"8ebf0919-6886-400b-9aec-0f38a55d6d3e","order_by":0,"name":"Deasy Mukti","email":"data:image/png;base64,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","orcid":"","institution":"Hasanuddin University","correspondingAuthor":true,"prefix":"","firstName":"Deasy","middleName":"","lastName":"Mukti","suffix":""},{"id":600903846,"identity":"59fc9513-78bf-4e85-8837-2d6c605d28ea","order_by":1,"name":"Halmar Halide","email":"","orcid":"","institution":"Hasanuddin University","correspondingAuthor":false,"prefix":"","firstName":"Halmar","middleName":"","lastName":"Halide","suffix":""}],"badges":[],"createdAt":"2026-02-24 05:38:23","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8952979/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8952979/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":104403262,"identity":"06de3c03-5e7b-415d-bb32-849580e6dda1","added_by":"auto","created_at":"2026-03-11 12:17:52","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":441045,"visible":true,"origin":"","legend":"\u003cp\u003eillustrates the study area in South Sulawesi (red box) and highlights the locations of the three meteorological stations used in this research: the Paotere Maritime Meteorological Station (MKS) shown with a blue circle, the Hasanuddin Meteorological Station (HND) marked with a green circle, and the Masamba Meteorological Station (MSB) is red circle.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8952979/v1/299ca19f4370db398dca50b1.png"},{"id":104040703,"identity":"3a94cf68-d876-4599-87e8-2ff4dea917a2","added_by":"auto","created_at":"2026-03-06 04:26:51","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":119060,"visible":true,"origin":"","legend":"\u003cp\u003eGeneralized Extreme Value fits (a–c) and return level comparison (d) for annual maximum daily rainfall (2009–2025).\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8952979/v1/7359257cb4926d414d2b836e.png"},{"id":104402959,"identity":"b70db923-fa28-4c09-80a7-35c811f38d26","added_by":"auto","created_at":"2026-03-11 12:17:00","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":194266,"visible":true,"origin":"","legend":"\u003cp\u003eAnnual Mean Rainfall Trend Analysis (2009–2025) for three South Sulawesi Meteorological Stations: (a) Hasanuddin Station (HND), (b) Makassar Station (MKS), and (c) Masamba Station (MSB).\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-8952979/v1/eec50184467216332caf6fde.png"},{"id":104402376,"identity":"25d4be07-c1cc-4736-820e-2af2e81c41af","added_by":"auto","created_at":"2026-03-11 12:15:11","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":183158,"visible":true,"origin":"","legend":"\u003cp\u003eRelationships between coastal Urban Heat Island intensity (UHICoast) and daily rainfall (left panels) and relative humidity (right panels) at Lag 0 for Tmin, Tmean, and Tmax.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-8952979/v1/49af60e194d83a6309dde6aa.png"},{"id":104040705,"identity":"eed5c71f-3a51-4b4e-a701-fcddeb43adb0","added_by":"auto","created_at":"2026-03-06 04:26:52","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":119672,"visible":true,"origin":"","legend":"\u003cp\u003eSame as Fig. 4, but for the urban–airport station (UHIAirport).\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-8952979/v1/dba645160cf73c5e7703553d.png"},{"id":105035867,"identity":"72f56f53-ed89-4bd9-83aa-4c38cb98541b","added_by":"auto","created_at":"2026-03-20 07:26:48","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1683080,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8952979/v1/424790e5-5b10-4202-b36c-51d63b11077a.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Linking Urban Heat Island Intensification with Rainfall Variability in Tropical Archipelagic Indonesia","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eClimate change is a multidimensional global challenge that disrupts hydrometeorological systems, ecosystems, and socio-economic stability worldwide (IPCC \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). In tropical developing nations such as Indonesia, these impacts are intensified by rapid urbanization and land-use change, which alter surface energy exchanges and local climatic balances (Zhou et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Winsemius et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Yet, despite extensive research on rainfall variability and the Urban Heat Island (UHI) effect individually, their combined dynamics remain underexplored in tropical archipelagic contexts. This study advances the field by providing one of the first integrated assessments of rainfall\u0026ndash;UHI interactions in Indonesia, highlighting how urbanization-driven warming influences local rainfall regimes. By focusing on South Sulawesi, we demonstrate the novelty of linking urban climate modification with hydrometeorological variability in a region where both processes critically shape resilience, water security, and public health (Ward et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Clarke et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eRainfall variability in the tropics often manifests through shifts in monsoon onset, rainfall intensity, and extreme precipitation frequency (Bl\u0026ouml;schl et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Nicholls et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). Indonesia\u0026rsquo;s coastal and archipelagic environments are especially vulnerable due to ocean\u0026ndash;atmosphere interactions and complex topography (Shen et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Meanwhile, UHI intensification\u0026mdash;driven by impervious surfaces and vegetation loss\u0026mdash;has raised local temperatures, altered humidity, and reshaped convective rainfall dynamics (Zhou et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Ward et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). These processes represent a compound interaction of global climate drivers and local anthropogenic forcing.\u003c/p\u003e \u003cp\u003eRecent studies across Asia highlight reciprocal linkages between UHI and rainfall variability, including the \u0026ldquo;rain island\u0026rdquo; phenomenon where urban warming enhances localized convection and precipitation (Zhang et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Li et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Evidence from China and India shows urbanization reshaping rainfall distribution and amplifying hydrometeorological risks during monsoon seasons (Wang et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Sati et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Such findings underscore the need for integrated approaches that combine meteorological, spatial, and socio-economic perspectives in tropical developing contexts.\u003c/p\u003e \u003cp\u003eSouth Sulawesi, Indonesia, offers a compelling case to examine these dynamics. The province\u0026rsquo;s monsoonal rainfall, rapid coastal urbanization, and diverse topography (Ward et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) intersect with economic sectors highly dependent on climatic stability (Clarke et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Rainfall fluctuations challenge agriculture, while rising urban temperatures exacerbate energy demand, heat-related illness, and reduced labor efficiency (Gupta et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Understanding rainfall\u0026ndash;UHI interactions is therefore vital for both scientific insight and practical adaptation in water management, land-use planning, and sustainable development.\u003c/p\u003e \u003cp\u003eDespite growing attention, few studies quantitatively assess rainfall and UHI together in tropical archipelagic regions (Clarke et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Shen et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Most analyze precipitation or temperature trends separately, leaving their mutual dynamics underexplored. This study addresses that gap by jointly examining rainfall and temperature records from three BMKG stations in South Sulawesi\u0026mdash;Hasanuddin (urban\u0026ndash;airport), Paotere/Makassar (coastal urban), and Andi Jemma/Masamba (rural inland). Using Mann\u0026ndash;Kendall tests, Sen\u0026rsquo;s slope estimation, and Generalized Extreme Value (GEV) modeling, we provide one of the first integrated empirical assessments of rainfall\u0026ndash;UHI interactions in Indonesia\u0026rsquo;s tropical archipelagic setting. By explicitly linking urbanization-driven warming with rainfall variability, this research contributes novel evidence to strengthen regional climate resilience frameworks and inform adaptation strategies across rapidly developing tropical regions.\u003c/p\u003e \u003cp\u003eThis paper is structured as follows. Section \u003cspan refid=\"Sec2\" class=\"InternalRef\"\u003e2\u003c/span\u003e outlines the methodological framework, including a description of the study area and data sources, the data preprocessing steps and statistical overview, inter-station comparison and trend detection procedures, as well as the analytical approaches used to examine temperature, Urban Heat Island (UHI) characteristics, and broader climate variability. Section \u003cspan refid=\"Sec7\" class=\"InternalRef\"\u003e3\u003c/span\u003e presents the results and discussion, covering rainfall extremes and the application of the Generalized Extreme Value (GEV) distribution, return period estimation, rainfall trends and spatial variability, UHI patterns, and a combined interpretation of UHI\u0026ndash;rainfall interactions and UHI trend behavior. Section \u003cspan refid=\"Sec14\" class=\"InternalRef\"\u003e4\u003c/span\u003e provides the conclusion of the study.\u003c/p\u003e"},{"header":"2 Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n \u003ch2\u003e2.1 Study Area and Data Sources\u003c/h2\u003e\n \u003cp\u003eThe analysis draws on daily rainfall and air temperature observations from three meteorological stations operated by the Meteorology, Climatology, and Geophysics Agency of Indonesia (BMKG) in South Sulawesi Province. These sites were selected to represent contrasting degrees of urbanization and topographic settings: Hasanuddin (HND), an urban\u0026ndash;airport environment; Paotere/Makassar (MKS), a coastal urban zone; and Andi Jemma/Masamba (MSB), a rural inland station used as the thermal reference. The datasets span January 2009 to August 2025 and include minimum, maximum, and mean temperature variables alongside rainfall totals. All records underwent quality assurance procedures to remove missing, duplicate, or anomalous entries prior to analysis.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\n \u003ch2\u003e2.2 Data Preprocessing and Statistical Overview\u003c/h2\u003e\n \u003cp\u003eData from each station were standardized into a consistent time-series structure consisting of date and measurement variables. Missing entries were eliminated using MATLAB\u0026rsquo;s rmmissing() function, and all date formats were unified via datetime conversion to ensure temporal consistency. Rainfall data were imported from \u003cem\u003erainfall09-25.xlsx\u003c/em\u003e, while temperature records were sourced from station-specific text files (\u003cem\u003eCoastal.xlsx, Airport.xlsx, and Remote.xlsx\u003c/em\u003e). Descriptive statistics, including mean, median, standard deviation, skewness, and kurtosis, were computed to characterize the statistical behavior of rainfall. Normality was assessed using the Kolmogorov\u0026ndash;Smirnov (KS) test to determine the suitability of parametric (t-test) or nonparametric (Mann\u0026ndash;Whitney U) methods for inter-station comparisons.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\n \u003ch2\u003e2.3 Inter-Station Comparison and Trend Detection\u003c/h2\u003e\n \u003cp\u003ePairwise comparisons were conducted to evaluate statistical differences among the three stations. Normally distributed data were tested using the t-test, while non-normal data employed the Mann\u0026ndash;Whitney U test, with variance homogeneity evaluated via MATLAB\u0026rsquo;s vartest2(). Correlation analyses employed Pearson\u0026rsquo;s (r) and Spearman\u0026rsquo;s (\u0026rho;) coefficients to capture linear and monotonic associations, respectively, with MSB serving as the rural reference site. Long-term rainfall trends were quantified using both linear regression and the nonparametric Mann\u0026ndash;Kendall (MK) test. The regression model \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\((Y={\\beta}_{0}+{\\beta}_{1}t+\\epsilon)\\)\u003c/span\u003e\u003c/span\u003e provided estimates of trend magnitude (\u0026beta;₁), standard error, confidence intervals, and explained variance (R\u0026sup2;), while the MK test identified the direction and significance of monotonic changes independent of distributional assumptions.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\n \u003ch2\u003e2.4 Temperature, UHI, and Climate Variability Analysis\u003c/h2\u003e\n \u003cp\u003eWhen rainfall patterns among stations showed no significant divergence, additional analyses were undertaken to examine the Urban Heat Island (UHI) effect. UHI intensity was calculated on a daily basis as\u003c/p\u003e\n \u003cdiv id=\"Equa\" class=\"Equation\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e$$UHI\\left(t\\right)={T}_{urban}\\left(t\\right)-{T}_{rural}\\left(t\\right)$$\u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003ewhere the Masamba station (MSB) serves as the rural thermal reference. This formulation allows UHI variability to be evaluated at daily resolution using Tmin, Tmean, and Tmax. The analysis does not assume a unidirectional causal influence of UHI on rainfall or humidity; instead, it focuses on their coupled and immediate co-variability under humid tropical coastal conditions.\u003c/p\u003e\n \u003cp\u003eAnnual temperature trends for Tmin, Tmax, and Tmean were subsequently derived using linear regression models of the form \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\u003cimg src=\"data:image/png;base64,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\"\u003e\u003c/span\u003e\u003c/span\u003e where the slope (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\beta\\)\u003c/span\u003e\u003c/span\u003e) represents the annual rate of change (\u0026deg;C yr⁻\u0026sup1;). Identical procedures were applied to estimate temporal trends in UHI intensity. All analyses were performed in MATLAB R2023b (Windows 10) using functions \u003cem\u003efitlm()\u003c/em\u003e, \u003cem\u003ecorr()\u003c/em\u003e, and \u003cem\u003ekstest()\u003c/em\u003e, supplemented by custom scripts for Mann\u0026ndash;Kendall and UHI computations. Statistical significance was evaluated at the 95% confidence level (\u0026alpha;\u0026thinsp;=\u0026thinsp;0.05). Outputs were provided in tabular and graphical formats\u0026mdash;including \u003cem\u003erainfall_trend_results.csv\u003c/em\u003e and \u003cem\u003eTren_Suhu_UHI.xlsx\u003c/em\u003e\u0026mdash;with visualizations comprising time-series plots, regression lines, and 95% confidence intervals.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"3 Results and Discussion","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Annual Maximum Rainfall and GEV Modeling\u003c/h2\u003e \u003cp\u003eThe annual maximum daily rainfall series (2009\u0026ndash;2025) were fitted to the Generalized Extreme Value (GEV) distribution (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea\u0026ndash;c). The urban\u0026ndash;airport station (Hasanuddin) exhibits the heaviest upper tail, with several events exceeding 250 mm and a clear right-skewed distribution. The coastal urban station (Makassar) shows moderate extremes (mostly 120\u0026ndash;180 mm), while the rural inland station (Masamba) displays the lowest magnitudes and least dispersion (modal value 100\u0026ndash;120 mm).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe rainfall extremes at the coastal MKS station (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb) exhibit a somewhat narrower distribution, with most values ranging between 120 and 180 mm. The fitted GEV curve indicates a predominance of moderate-intensity extremes, reflecting Makassar\u0026rsquo;s maritime climatic setting where interactions between land\u0026ndash;sea breezes and moist marine air masses modulate convective development (Qian et al., 2013). Compared with HND, the shorter tail at MKS suggests a lower likelihood of exceptionally high rainfall events.\u003c/p\u003e \u003cp\u003eThe MSB station (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ec), situated inland at higher elevation, shows the lowest range of annual maximum rainfall, with a modal value of approximately 100\u0026ndash;120 mm. The sharper and less skewed GEV profile suggests lower interannual variability and reduced occurrence of extreme rainfall. This aligns with findings that inland highland or orographic regions in Indonesia often experience more stable precipitation regimes dominated by topographic lifting rather than intense convective systems (Hidayat \u0026amp; Ando, 2014).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Return Level Analysis\u003c/h2\u003e \u003cp\u003eReturn level plots (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ed) confirm substantial spatial divergence. At the 50-year return period, estimated rainfall intensities are 330 mm (Hasanuddin), 300 mm (Makassar), and 210 mm (Masamba). The elevated extremes at the urban\u0026ndash;airport site support the presence of a \u0026ldquo;rain island\u0026rdquo; effect driven by enhanced surface heating, roughness, and boundary-layer instability (Zhang et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Li et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eGEV parameters and selected return levels (2009\u0026ndash;2025)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLocation (\u0026micro;)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eScale (σ)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eShape (ξ)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10 year (mm)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e50 year (mm)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e100 year (mm)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHasanuddin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e143.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e48.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e255\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e330\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e365\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMakassar\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e136.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e41.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e235\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e330\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMasamba\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e112.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e32.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e175\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e210\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e230\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Long-Term Rainfall Trends\u003c/h2\u003e \u003cp\u003eMann\u0026ndash;Kendall tests and Sen\u0026rsquo;s slope estimates reveal no statistically significant trends in annual or seasonal rainfall totals at any station (p\u0026thinsp;\u0026gt;\u0026thinsp;0.4; Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Interannual variability is dominated by the Asian\u0026ndash;Australian monsoon and ENSO/IOD phases, consistent with previous regional studies (Supari et al. 2017).\u003c/p\u003e \u003cp\u003eThe three daily rainfall series (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea\u0026ndash;c) reveal a distinct set of characteristics that complement the statistical finding of no significant long-term trends. A prominent feature across all stations is the strong and repetitive seasonal cycle, with rainfall peaks occurring almost annually. This pattern reflects the dominant influence of the Austral\u0026ndash;Asian monsoon system, where the wet season intensifies through strengthened northwesterly monsoon flow and the development of localized convergence zones. Such a consistent seasonal signature suggests that short-term monsoonal dynamics exert a stronger control over rainfall variability than any long-term climatic trend (Aldrian and Susanto 2003; Qian et al. 2013).\u003c/p\u003e \u003cp\u003eAmong the three sites, the Hasanuddin station (HND) exhibits the most pronounced variability, with sharper fluctuations and more frequent high-intensity events. This behaviour is likely linked to its low-elevation coastal setting combined with interactions within the surrounding urban\u0026ndash;airport boundary layer, which can enhance localized convective development. Previous studies have shown that coastal urban areas often display elevated convective rainfall variability due to increased surface roughness and alterations to boundary-layer airflow (Kusumaningtyas et al. 2021; Li et al. 2020).\u003c/p\u003e \u003cp\u003eIn contrast, rainfall at the Paotere\u0026ndash;Makassar station (MKS) demonstrates a more uniform pattern with no evident rise in extreme event magnitudes, aligning with its high Mann\u0026ndash;Kendall p-value (p\u0026thinsp;\u0026gt;\u0026thinsp;0.8). This stability reinforces the notion that Makassar\u0026rsquo;s coastal environment is driven predominantly by short-term land\u0026ndash;sea interactions rather than long-term atmospheric shifts. Previous regional research similarly reports that rainfall along coastal South Sulawesi is more sensitive to diurnal sea\u0026ndash;land breeze mechanisms than to persistent climate-scale forcing (Juneng and Tangang 2005; Pratikto et al. 2022).\u003c/p\u003e \u003cp\u003eThe Masamba station (MSB), situated at a higher elevation, records generally lower rainfall intensities, yet still displays clear annual fluctuations. This pattern is typical of upland and valley regions where rainfall production is strongly modulated by orographic lifting and local topographic effects. Studies in northern and southern Sulawesi similarly report that highland rainfall variability is primarily governed by terrain-driven processes rather than long-term climatic tendencies (Sulistyowati et al. 2020; Hidayat and Ando 2014).\u003c/p\u003e \u003cp\u003eTaken together, the absence of statistically significant long-term rainfall trends across all stations supports the broader understanding that rainfall variability in the Indonesian Maritime Continent is largely shaped by interannual modes such as ENSO and the Indian Ocean Dipole (IOD). Numerous climatological assessments highlight that, while extreme rainfall events may evolve in response to localized atmospheric\u0026ndash;oceanic feedbacks, the mean rainfall in this region often shows weak or insignificant trends over multi-decadal periods (Chang et al. 2005; Supari et al. 2017).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eStatistical Results of Daily Rainfall Trend Analysis (2009\u0026ndash;2025) at Three Meteorological Stations in South Sulawesi\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTrend (mm/day)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(SE)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({R}^{2}\\)\u003c/span\u003e\u003c/span\u003e (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHND\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.000414962\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.000159079\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.009115469\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.00111698\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMKS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.000053092\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.000147056\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.718089353\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.00002142\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMSB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.000302276\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.000141958\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.033266880\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.00074456\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Urban Heat Island Intensity and Trends\u003c/h2\u003e \u003cp\u003eAlthough no statistically significant long-term trends are detected in mean or seasonal rainfall totals (Section \u003cspan refid=\"Sec10\" class=\"InternalRef\"\u003e3.3\u003c/span\u003e), the marked spatial divergence in extreme rainfall magnitudes\u0026mdash;particularly the elevated return levels at the urban\u0026ndash;airport station\u0026mdash;motivates an examination of concurrent modifications in local temperature regimes through the Urban Heat Island (UHI) effect. Here, UHI is treated as a diagnostic indicator of urban\u0026ndash;rural thermal contrast rather than a direct forcing mechanism, consistent with recent urban climate assessments emphasizing coupled land\u0026ndash;atmosphere interactions under climate change (IPCC \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Zhou et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eUHI intensity, defined relative to the rural Masamba station, exhibits strong spatial heterogeneity across South Sulawesi (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The Hasanuddin urban\u0026ndash;airport site shows a consistently positive and statistically significant UHI signal, with an annual mean intensity of +\u0026thinsp;1.58\u0026deg;C (Tmean) and +\u0026thinsp;2.11\u0026deg;C (Tmax). Moreover, the UHI trend is significantly increasing at rates of 0.042\u0026deg;C yr⁻\u0026sup1; (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) for Tmean and 0.055\u0026deg;C yr⁻\u0026sup1; (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) for Tmax, indicating a robust intensification of urban warming over time. In contrast, the coastal Makassar station displays a slightly negative mean UHI (\u0026ndash;0.42\u0026deg;C), with a weak and statistically non-significant trend (\u0026ndash;0.018\u0026deg;C yr⁻\u0026sup1;, p\u0026thinsp;=\u0026thinsp;0.09), suggesting that maritime influences largely offset classical daytime urban warming.\u003c/p\u003e \u003cp\u003eTrend analysis further reveals contrasting temporal evolutions of UHI intensity between stations. At Hasanuddin, both Tmean- and Tmax-based UHI exhibit statistically significant positive trends, reaching up to +\u0026thinsp;0.055\u0026deg;C yr⁻\u0026sup1; over the 2009\u0026ndash;2025 period. This gradual intensification is consistent with increasing urban\u0026ndash;rural thermal contrast associated with continued land-surface modification. However, this signal must be interpreted in conjunction with the pronounced warming observed at the rural reference site, particularly in Tmin (R\u0026sup2; = 0.311), which reflects broader regional climate warming affecting both urban and rural environments (IPCC \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). As highlighted in previous studies, warming at rural reference stations can partially mask future UHI amplification if background climate change is not explicitly accounted for (Li et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Zhou et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe muted or negative UHI at the coastal Makassar station underscores the dominant role of local physical controls specific to humid tropical coastlines. Persistent sea-breeze circulation, high atmospheric moisture content, and frequent cloud cover act to limit daytime radiative heating while enhancing latent heat flux, thereby suppressing the development of a classical urban heat island (Zhang et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Wang et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Similar coastal UHI weakening has been documented in other maritime tropical cities, where moisture availability and advective cooling outweigh the warming effects of urban surfaces (Kim and Baik \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Zhou et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTaken together, the pronounced UHI signal at the inland urban\u0026ndash;airport site and the suppressed or absent UHI along the immediate coast delineate two distinct urban thermal regimes within the same metropolitan region. This spatial contrast provides the physical context for interpreting the daily-scale coupling between UHI intensity, rainfall, and relative humidity examined in the following section. In particular, it suggests that in South Sulawesi, UHI variability is highly sensitive to atmospheric moisture conditions and short-term hydro-meteorological variability, preconditioning the negative UHI\u0026ndash;rainfall and UHI\u0026ndash;humidity relationships identified at daily timescales.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eScatterplot showing Coast UHI(Tmin) versus rainfall (Lag 0). A significant negative slope (β = \u0026minus;\u0026thinsp;0.0091, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) demonstrates that increased rainfall weakens the urban\u0026ndash;rural temperature contrast. Rainfall-induced evaporative cooling and cloud cover likely reduce UHI formation at the coastal site.\u003c/p\u003e \u003cp\u003eScatterplot showing Coast UHI(Tmin) versus relative humidity (Lag 0). The relationship is weak and statistically insignificant (β = \u0026minus;\u0026thinsp;0.0036, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.289), suggesting that nighttime UHI at the coast is less sensitive to humidity variability compared to the airport station.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eScatterplot showing the linkage between Airport UHI(Tmin) and daily rainfall (Lag 0). The regression slope is negative (β = \u0026minus;\u0026thinsp;0.0072, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), indicating that higher rainfall is associated with weaker nighttime UHI intensity. The distribution shows wide variability at low rainfall but a consistent downward trend as rainfall increases.\u003c/p\u003e \u003cp\u003eScatterplot of Airport UHI(Tmin) versus daily relative humidity (Lag 0). The regression slope is positive (β = +0.0531, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), indicating that nighttime UHI intensity slightly increases under higher humidity, likely due to enhanced longwave trapping and reduced nighttime cooling\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMean UHI intensity and linear trends (2009\u0026ndash;2025)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUHI definition\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean UHI (\u0026deg;C)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTrend (\u0026deg;C yr⁻\u0026sup1;)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eR\u0026sup2;\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHasanuddin \u0026ndash; Masamba (Tmean)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.042\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.28\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMakassar \u0026ndash; Masamba (Tmean)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026ndash;0.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026ndash;0.018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHasanuddin \u0026ndash; Masamba (Tmax)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.055\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.34\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Daily-Scale Modulation of UHI by Rainfall and Humidity\u003c/h2\u003e \u003cp\u003eAt the daily scale, UHI intensity at both the airport and coastal stations exhibits a consistent and statistically significant negative relationship with rainfall and relative humidity. For Tmax- and Tmean-based UHI, regression coefficients linking UHI to rainfall and humidity are significantly negative at Lag 0 (e.g., Airport Tmax: β_Rain \u0026asymp; \u0026minus;\u0026thinsp;0.015, β_RH \u0026asymp; \u0026minus;\u0026thinsp;0.092; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), with Spearman correlations ranging from \u0026minus;\u0026thinsp;0.54 to \u0026minus;\u0026thinsp;0.66.\u003c/p\u003e \u003cp\u003eThese relationships weaken rapidly beyond a one-day lag, indicating that the suppression of UHI is primarily an immediate response to rainfall-induced evaporative cooling, increased cloud cover, and enhanced latent heat fluxes. The coastal station exhibits the strongest negative coupling, reflecting continuous moisture advection from the adjacent sea and frequent cloudiness that constrain radiative heating.\u003c/p\u003e \u003cp\u003eAn exception is observed for Tmin-based UHI at the airport station, which shows a weak positive association with humidity. This behavior is consistent with nocturnal radiative trapping under humid conditions and reduced longwave cooling, indicating distinct controls on daytime versus nighttime UHI dynamics.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.6 Synthesis: Rain Island alongside Moisture-Suppressed UHI\u003c/h2\u003e \u003cp\u003eThe urban climate of South Sulawesi therefore exhibits two coexisting but physically consistent phenomena. First, a localized enhancement of extreme daily rainfall is detected at the urban\u0026ndash;airport site, consistent with processes often associated with urban surface roughness, thermal perturbations, and boundary-layer modification. Second, UHI intensity is strongly constrained by moisture availability, producing a moisture-suppressed UHI regime in which wet and humid conditions limit daytime urban heating.\u003c/p\u003e \u003cp\u003eThis dual behavior reconciles contrasting findings in the literature. While numerous studies document urban-enhanced convection and rainfall under favorable thermodynamic conditions, others report urban-induced drying or suppressed convection in humid coastal or marine-influenced environments. Makassar clearly aligns with the latter category, emphasizing the context dependence of UHI\u0026ndash;rainfall interactions in tropical coastal settings.\u003c/p\u003e \u003c/div\u003e"},{"header":"4 Conclussion","content":"\u003cp\u003eThis study provides observational evidence from a tropical archipelagic setting demonstrating that urbanization can simultaneously intensify rainfall extremes while constraining Urban Heat Island development. Although mean rainfall in South Sulawesi shows no significant long-term trend, extreme daily precipitation is substantially amplified at the urban\u0026ndash;airport site, with 50-year return levels approximately 60% higher than those at the rural inland reference station. At the same time, UHI intensity is strongly modulated by moisture availability and coastal circulation, resulting in a modest but significant positive UHI confined to inland-urban environments and near-zero or negative anomalies along the immediate coast.\u003c/p\u003e \u003cp\u003eThe pronounced negative coupling between UHI intensity, rainfall, and relative humidity at daily time scales indicates that humid tropical coastal cities operate under a moisture-limited UHI regime, where wet conditions act as an effective brake on urban overheating. These findings reconcile apparently conflicting urban climate paradigms and underscore the necessity of region-specific interpretations of UHI\u0026ndash;rainfall interactions. For rapidly growing coastal cities such as Makassar, climate adaptation strategies must address the compound risk of intensified rainfall extremes alongside moderated\u0026mdash;yet still relevant\u0026mdash;heat stress, rather than assuming continental-style UHI amplification.\u003c/p\u003e \u003cp\u003e \u003cb\u003eImplications for Urban Climate Adaptation\u003c/b\u003e \u003c/p\u003e \u003cp\u003eGiven the dual signature identified in this study \u0026mdash; intensified extreme rainfall alongside a strongly moisture-suppressed Urban Heat Island in a humid tropical coastal city \u0026mdash; the following targeted actions are recommended:\u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eFlood-risk mapping and early-warning systems for Makassar and similar archipelagic cities must be updated to reflect the observed 50\u0026ndash;60% higher return levels of daily extreme rainfall in urban and peri-urban zones, even in the absence of significant mean rainfall trends. Current design standards based on rural or historical rural-reference statistics will substantially underestimate flood hazard.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eUHI mitigation strategies in coastal Sulawesi and comparable Southeast Asian cities should prioritize moisture-retentive and evaporative cooling solutions (urban parks, green roofs, blue\u0026ndash;green corridors, water-retentive pavements) over purely reflective \u0026ldquo;cool materials\u0026rdquo;. Our results show that increasing latent heat flux is more effective at suppressing the (already modest) daytime UHI, whereas high-albedo surfaces alone may be less beneficial in persistently humid environments.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eFuture urban expansion planning around Hasanuddin International Airport and other inland-urban zones requires explicit consideration of \u0026ldquo;rain island\u0026rdquo; amplification. New impervious developments should be coupled with large-scale retention basins and upstream reforestation to offset enhanced convective extremes.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eObservational networks need at least one additional high-quality rural reference station located\u0026thinsp;\u0026gt;\u0026thinsp;30\u0026ndash;40 km inland and at similar elevation to Masamba, but outside the expanding urban plume, to prevent underestimation of future UHI trends as the current rural sites themselves warm.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eModeling and attribution studies should employ convection-permitting regional climate models with explicit urban canopy parameterization and sea-breeze representation to quantify the separate contributions of surface imperviousness, aerosol effects, and sea-breeze dynamics to the observed rainfall intensification and UHI suppression.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003cp\u003eImplementing these context-specific measures will substantially improve compound heat-flood resilience in rapidly urbanizing tropical archipelagic regions where classical continental UHI paradigms do not apply.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDeasy Mukti (DM): Conceptualization, Methodology, Software, Formal analysis, Investigation, Data curation, Writing \u0026ndash; original draft, Visualization. Halmar Halide (HH): critically revised the article for final approval.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAll authors reviewed and approved the final version of the manuscript and agree to be accountable for all aspects of the work.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of Interest\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;The authors state that there are no conflicts of interest related to this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;This study was conducted without financial support from any public, commercial, or non-profit funding organizations.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCoding and Data Availability Statement\u003c/strong\u003e\u003cbr\u003eBoth the datasets used and the MATLAB\u0026reg;\u0026nbsp;coding used in this work are available by request to DM.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;The authors express their gratitude to the Meteorology, Climatology, and Geophysics Agency for providing the rainfall and temperature datasets. The authors also acknowledge the use of ChatGPT (developed by OpenAI) as a supporting tool during the manuscript preparation process.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eUse of AI Tools\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;ChatGPT (OpenAI) and Grok (xAI) were employed to assist with language editing, clarity enhancement, and formatting during manuscript preparation. All outputs generated with the assistance of AI tools were carefully reviewed and validated by the authors to ensure scientific accuracy and integrity.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eBl\u0026ouml;schl G, Hall J, Viglione A, Perdig\u0026atilde;o RAP, Parajka J, Merz B, Arheimer B et al (2019) Changing climate shifts timing of European floods. Science 365(6451):588\u0026ndash;590. https://doi.org/10.1126/science.aan2506\u003c/li\u003e\n \u003cli\u003eClarke L, Jiang K, Akimoto K, Babiker M, Blanford G, Fisher-Vanden K, Zhou N et al (2023) Climate Change 2023: Mitigation of Climate Change. Contribution of Working Group III to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press\u003c/li\u003e\n \u003cli\u003eGupta J, van der Grijp N, Kuik O (2021) Climate change, development and sustainability: A critical review. Environ Sci Policy 124:1\u0026ndash;12. https://doi.org/10.1016/j.envsci.2021.05.002\u003c/li\u003e\n \u003cli\u003eIPCC (2021) Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press\u003c/li\u003e\n \u003cli\u003eKim YH, Baik JJ (2002) Maximum urban heat island intensity in Seoul. J Appl Meteorol 41(6):651\u0026ndash;659. https://doi.org/10.1175/1520-0450(2002)041\u0026lt;0651:MUHIII\u0026gt;2.0.CO;2\u003c/li\u003e\n \u003cli\u003eLi D, Sun T, Liu M, Yang L, Wang L, Gao Z (2021) Contrasting responses of urban and rural surface energy budgets to heatwaves explain the urban heat island amplification. Nat Commun 12:2620. https://doi.org/10.1038/s41467-021-22875-0\u003c/li\u003e\n \u003cli\u003eLi H, Zhou Y, Jia G, Zhao K, Dong J (2021) Quantifying the response of surface urban heat island to urbanization using the annual temperature cycle model. Geosci Front 13(1):101141. https://doi.org/10.1016/j.gsf.2021.101141\u003c/li\u003e\n \u003cli\u003eLin CY, Chen WC, Chang PL, Sheng YF (2011) Impact of the urban heat island effect on precipitation over a complex geographic environment in northern Taiwan. J Appl Meteorol Climatol 50(2):339\u0026ndash;353. https://doi.org/10.1175/2010JAMC2504.1\u003c/li\u003e\n \u003cli\u003eNicholls RJ, Hanson S, Herweijer C, Patmore N, Hallegatte S, Corfee-Morlot J, Ch\u0026acirc;teau J, Muir-Wood R (2008) Ranking port cities with high exposure and vulnerability to climate extremes: Exposure estimates. OECD Environment Working Papers No 1. OECD Publishing. https://doi.org/10.1787/011766488208\u003c/li\u003e\n \u003cli\u003eOtto FEL, Harrington L, Schmitt K, Philip S, Kew S, van Oldenborgh GJ, Singh R, Kimutai J, Wolski P (2020) Challenges to understanding extreme weather changes in lower-income countries. Bull Am Meteorol Soc 101(10):E1851\u0026ndash;E1860. https://doi.org/10.1175/BAMS-D-19-0317.1\u003c/li\u003e\n \u003cli\u003eSati AP, Singh RB, Chandel V (2024) Urbanization and monsoon rainfall variability in Indian megacities: Emerging risks under climate change. Clim Dyn 63(4):1453\u0026ndash;1470. https://doi.org/10.1007/s00382-024-06874-2\u003c/li\u003e\n \u003cli\u003eShepherd JM (2005) A review of current investigations of urban-induced rainfall and recommendations for the future. Earth Interact 9(12):1\u0026ndash;27. https://doi.org/10.1175/EI156.1\u003c/li\u003e\n \u003cli\u003eShen Y, Liu J, Li J (2023) Compound flood risks in tropical regions under changing climate and urbanization: A review. Earths Future 11(5):e2023EF003020. https://doi.org/10.1029/2023EF003020\u003c/li\u003e\n \u003cli\u003eVasconcelos Junior FdC, Zachariah M, Silva TLdV, dos Santos EP, Coelho CAS, Alves LM, Otto FEL et al (2024) An attribution study of very intense rainfall events in Eastern Northeast Brazil. Weather Clim Extrem 45:100699. https://doi.org/10.1016/j.wace.2024.100699\u003c/li\u003e\n \u003cli\u003eWang J, Zhang M, Guo Y (2017) Effects of urbanization on precipitation over the Yangtze River Delta: Dynamic and thermodynamic mechanisms. J Geophys Res Atmos 122(13):7277\u0026ndash;7290. https://doi.org/10.1002/2017JD026774\u003c/li\u003e\n \u003cli\u003eWard PJ, Jongman B, Weiland FS, Bouwman A, van Beek R, Bierkens MFP, Ligtvoet W, Winsemius HC (2020) Assessing flood risk at the global scale: Model setup, results, and sensitivity. Environ Res Lett 15(3):034015. https://doi.org/10.1088/1748-9326/ab7e2a\u003c/li\u003e\n \u003cli\u003eWinsemius HC, Aerts JCJH, van Beek LPH, Bierkens MFP, Bouwman A, Jongman B, Ward PJ et al (2016) Global drivers of future river flood risk. Nat Clim Change 6(4):381\u0026ndash;385. https://doi.org/10.1038/nclimate2893\u003c/li\u003e\n \u003cli\u003eYang H, Wu Z, Dawson RJ, Barr S, Ford A, Li Y (2024) Quantifying surface urban heat island variations and patterns: Comparison of two cities in three-stage dynamic rural\u0026ndash;urban transition. Sustain Cities Soc 109:105538. https://doi.org/10.1016/j.scs.2024.105538\u003c/li\u003e\n \u003cli\u003eZhang C, Chen F, Miao S (2019) Urbanization and rainfall change: Understanding the linkage through boundary-layer processes. J Clim 32(2):349\u0026ndash;367. https://doi.org/10.1175/JCLI-D-18-0328.1\u003c/li\u003e\n \u003cli\u003eZhou D, Zhao S, Zhang L, Liu S (2022) Remotely sensed assessment of urban heat island and its spatial-temporal dynamics: A review. Remote Sens Environ 273:112978. https://doi.org/10.1016/j.rse.2022.112978\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Urban Heat Island, rainfall extremes, tropical coastal climate, Generalized Extreme Value distribution, sea-breeze modulation, Indonesia","lastPublishedDoi":"10.21203/rs.3.rs-8952979/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8952979/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eUrbanization in humid tropical archipelagic environments alters surface energy balance and atmospheric moisture dynamics, yet the coupled behavior of Urban Heat Island (UHI) intensity and rainfall extremes in such settings remains poorly quantified. This study investigates the interactions between urban thermal amplification and precipitation variability using quality-controlled daily observations (2009–2025) from three BMKG stations in South Sulawesi, Indonesia, representing an urban–airport site (Hasanuddin), a coastal urban site (Paotere–Makassar), and a rural inland reference (Masamba).\u003c/p\u003e\n\u003cp\u003eWhile no statistically significant long-term trend is detected in mean rainfall across stations, extreme precipitation exhibits pronounced spatial differentiation. Generalized Extreme Value modeling reveals that the 50-year return level at the urban–airport site reaches approximately 330 mm, substantially exceeding the ~210 mm estimated at the rural station, indicating urban-enhanced rainfall extremes. Concurrently, a persistent positive UHI signal (+1.4 to +1.8 °C in annual mean temperature) is observed at the urban–airport site relative to the rural reference, whereas the coastal urban station shows near-neutral or slightly negative anomalies consistent with sea-breeze moderation and high moisture availability.\u003c/p\u003e\n\u003cp\u003eDaily-scale analyses demonstrate robust negative associations between UHI intensity and both rainfall and relative humidity (Spearman ρ ≈ –0.54 to –0.66, p \u0026lt; 0.001), suggesting that enhanced latent heat fluxes and cloud-related radiative effects suppress urban thermal contrast under wet conditions. These results indicate the coexistence of urban-amplified rainfall extremes (“rain island”) and a moisture-suppressed UHI regime, highlighting nonlinear feedbacks between thermal forcing and hydrological processes in tropical coastal cities.\u003c/p\u003e\n\u003cp\u003eBy providing one of the first integrated observational assessments of UHI–rainfall coupling in an archipelagic Southeast Asian context, this study advances understanding of urban hydroclimatic feedbacks and underscores the importance of incorporating moisture–thermal interactions into climate adaptation strategies, urban design, and flood-risk management in rapidly urbanizing tropical regions.\u003c/p\u003e","manuscriptTitle":"Linking Urban Heat Island Intensification with Rainfall Variability in Tropical Archipelagic Indonesia","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-06 04:26:47","doi":"10.21203/rs.3.rs-8952979/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"fe30fcdf-6598-4132-869b-ddba720cd8ea","owner":[],"postedDate":"March 6th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-03-19T21:39:04+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-06 04:26:47","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8952979","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8952979","identity":"rs-8952979","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}