# Enhanced short-duration precipitation and divergent rainfall spatial patterns of Typhoon Nida (2016) under the impact of urbanization

Xiaowen Huang, Long Yang, Laurent Z.X. Li, Dashan Wang, Maofeng Liu, Yibing Su, Zhenzhong Zeng

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## To cite this version:

Xiaowen Huang, Long Yang, Laurent Z.X. Li, Dashan Wang, Maofeng Liu, et al.. Enhanced short-duration precipitation and divergent rainfall spatial patterns of Typhoon Nida (2016) under the impact of urbanization. Sustainable Horizons, 2025, 16, pp.100160. 10.1016/j.horiz.2025.100160. hal-05378765

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https://hal.science/hal-05378765v1

Submitted on 23 Nov 2025

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Original Research Articles

# Enhanced short-duration precipitation and divergent rainfall spatial patterns of Typhoon Nida (2016) under the impact of urbanization

<figure><img src="image_3.png" /></figure>

Xiaowen Huang<sup>a,b,c</sup>, Long Yang<sup>d,e</sup>, Laurent Z.X. Li<sup>f</sup>, Dashan Wang<sup>a</sup>, Maofeng Liu<sup>g</sup>, Yibing Su<sup>h</sup>, Zhenzhong Zeng<sup>a,b,c,i,*</sup>

## ARTICLE INFO

**Keywords:**

Land-atmosphere interaction

Hydrometeorology

Tropical cyclone event

Urbanization

Coupled model simulation

## ABSTRACT

Tropical cyclones (TCs), when approaching coasts and after landfalling, may exhibit significant interactions with heavily urbanized surfaces. Despite numerous studies, the effects of urbanization on TCs and their landfalling processes remain insufficiently explored. Typhoon Nida, landfalling in the Pearl River Delta region on August 1, 2016, provides an interesting case for us to revisit the issue. We use the Weather Research and Forecasting model in paired numerical experiments to isolate the urban effects. Urban land cover has a small impact on the track and the intensity of the typhoon. Urban effect on landfalling precipitation in this event is mainly profound in changing rainfall spatial patterns rather than the average rain rate. The accumulated precipitation and the rain rate of the major urban area are reduced. During different stages of the TC rapid movement, enhanced precipitation is observed at the downwind and upwind regions within a short duration. The increase in surface temperature and sensible heat flux enhances thermal circulation; however, this effect is weakened under the strong synoptic background during landfall. Increasing urban surface roughness decelerates airflows and enhances updrafts along the urban boundaries, producing enhanced water vapor convergence and bifurcated winds, increasing downwind precipitation with a maximum value of 46.17 mm in 6 h. Throughout the TC rapid movement, urban modification on precipitation patterns displays high spatial variability over short periods. The study raises concern about more extreme hazards occurring in multiple locations over urban areas caused by urban-induced TC rainfall redistribution.

## 1. Introduction

Tropical cyclones (TCs) are among the most destructive weather systems during the warm season in East Asia, particularly along coastal regions, where they drive extreme precipitation and severe wind hazards (Utsumi et al., 2017). Over the past decades, observations have revealed a trend toward increasing TC intensity in East Asia's coastal areas, raising concerns about growing risks of extreme events, such as

extreme rainfall, floods, and strong winds (Chang et al., 2012; Park et al., 2014). This is especially alarming for China where TC-affected areas have been expanding steadily (Li et al., 2023). It is estimated that TC-induced precipitation contributes to over 80 % of China's TC-related economic loss, with the remaining losses attributed to TC winds and storm surges (Hu et al., 2023). TCs and their associated precipitation are regulated by complex processes, including moisture transport, the growth of mesoscale convective systems, boundary layer

<footnote><sup>a</sup> School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen, China</footnote>

<footnote><sup>b</sup> SUSTech Energy Institute for Carbon Neutrality, Southern University of Science and Technology, Shenzhen, China</footnote>

<footnote><sup>c</sup> State Key Laboratory of Soil Pollution Control and Safety, Southern University of Science and Technology, Shenzhen, China</footnote>

<footnote><sup>d</sup> School of Geography and Ocean Sciences, Nanjing University, Nanjing, China</footnote>

<footnote><sup>e</sup> Frontiers Science Center for Critical Earth Material Cycling, Nanjing University, Nanjing, China</footnote>

<footnote><sup>f</sup> Laboratoire de Météorologie Dynamique, Centre National de la Recherche Scientifique, Sorbonne Université, Ecole Normale Supérieure, Ecole Polytechnique, Paris, France</footnote>

<footnote><sup>g</sup> Department of Atmospheric and Oceanic Sciences, School of Physics, Peking University, Beijing, China</footnote>

<footnote><sup>h</sup> Civil and Environmental Engineering, Princeton University, NJ, USA</footnote>

<footnote><sup>i</sup> Shenzhen Key Laboratory of Precision Measurement and Early Warning Technology for Urban Environmental Health Risks, School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen, China</footnote>

<footnote>* Corresponding author.
E-mail address: zengzz@sustech.edu.cn (Z. Zeng).</footnote>
energy fluxes, and surface interactions (Chen et al., 2010; Shen et al., 2021). Notably, the local-scale variability of TC rainfall highlights the need to explore how local surface properties modulate precipitation (Mori et al., 2021). While most previous studies on surface-TC interactions have focused on topographic effects (Smith et al., 2009; Chang et al., 2013; Qiu et al., 2019), urban surfaces—an increasingly dominant land cover type in coastal China—have received less attention (Zhang et al., 2018). China’s unprecedented urbanization has led to the development of large coastal urban agglomerations, such as the Pearl River Delta (PRD), the Yangtze River Delta, and the Beijing-Tianjin-Hebei region (Iossifova, 2019; Utsumi and Kim, 2022). As global warming continues and the global hydrological cycle intensifies, these densely populated, socioeconomically concentrated urban areas face a greater threat of TC-related hazards (Mori et al., 2021).

Previous studies have highlighted urbanization's critical role in changing precipitation characteristics via land-atmosphere interactions. Based on in-situ data analysis and numerical simulation, the urban impact on precipitation can be attributed to several mechanisms, such as the thermal perturbation from the urban heat island (UHI) effects (Han and Baik, 2008), enhanced convergence due to urban canopy effects (Shem and Shepherd, 2009), and changes in radiation and cloud formation caused by urban aerosol effects (Rosenfeld et al., 2008). These mechanisms are interrelated and their synergistic effects increase the complexity of urban influence on changing the spatiotemporal patterns of precipitation (Fan et al., 2020). On one hand, urban extent increases precipitation by promoting vertical motions, triggering convection, and enhancing moisture convergence (Argüeso et al., 2016). On the other hand, impervious urban surface reduces evapotranspiration and moisture availability, decreases convective available potential energy, and thus suppressing precipitation (Wang et al., 2018). The impact of urban environment on precipitation varies under different synoptic conditions (Yang et al., 2021a), leading to more intricate rainfall processes. Yet such interactions remain understudied in the context of TC landfall, where strong synoptic backgrounds may reshape urban-driven precipitation mechanisms.

While urbanization's role in modifying precipitation via land-atmosphere interactions is recognized, its specific impact on TC precipitation—especially during the rapid landfall process—remains poorly understood, with diverse conclusions and mechanisms in existing research. Internationally studies have revealed divergent impacts of urbanization. Zhang et al. (2018) found that urbanization in the Houston metropolitan area substantially increased total rainfall of Hurricane Harvey (2017) and shifted its rainfall maximum, attributing this to enhanced low-level convergence and drag effect from increased surface roughness. Islam et al. (2023) analyzed five tropical storms that attack Eastern United States and concluded that urban effects vary among TCs, mainly determined by the storm magnitude. In China, where TC landfall frequency is the highest globally (Fudeyasu et al., 2014), studies on coastal metropolitans also disagree on the urban-induced rainfall patterns and dominant mechanisms. Ao et al. (2022) showed urbanization altered TC rainfall spatial distribution rather than its total amount. He et al. (2023) emphasized urban thermal perturbation-driven low-level moisture convergence as the key driver of TC precipitation changes. Meanwhile, Du et al. (2023) linked increased TC rainfall to urban surface friction. Previous studies mainly focus on total TC rainfall over urban areas or temporal rainfall variability during TC landfall, but overlook the spatial rainfall variability over urban areas during rapid TC movement. Additionally, the PRD—one of China’s most urbanized (the home to 2 of the top 30 cities; Iossifova, 2019) and TC-prone (TCs contribute 20%–30% of annual rainfall; Wang et al., 2016) coastal regions—has seen limited targeted research (Yang et al., 2018; He et al., 2023), leaving gaps in understanding urban-TC interactions in this high-risk area.

This study addresses these gaps by focusing on Typhoon Nida (2016)—the strongest TC to hit the PRD since 1949, which made direct

landfall on August 1, 2016, and traversed the entire PRD region, bringing heavy precipitation and substantial economic losses (Wang et al., 2018a). Its rapid movement and direct passage through the PRD’s urban core make it an ideal case to isolate urban impacts on TC precipitation and investigate their interaction on urban rainfall spatial variability. By designing contrasting experiments with and without urban land cover, we aim to address the following research questions: (1) To what extent does urbanization influence the track and intensity of Nida (2016) during the overall landfall process? (2) What are the impacts of urbanization on the spatial and temporal landfalling precipitation patterns of Nida (2016)? (3) What are the dominant mechanisms driving these impacts?

## 2. Materials and methods

### 2.1. Model configuration

We adopted version 4.2.2 of the Advance Research Weather Research and Forecasting (WRF-ARW) model for numerical simulations (https://github.com/wrf-model/WRF/releases). Developed by the National Center for Atmospheric Research (NCAR), WRF is a complicated three-dimensional, non-hydrostatic mesoscale model that focuses on weather systems spanning a few kilometers to a few hundred kilometers, which is suitable for broad applications including parameterization research, regional climate research, and hurricane research (Skamarock et al., 2019). The model was configured with three two-way nested domains (D01, D02, and D03), containing horizontal grid cells of 160 × 133, 189 × 165, and 135 × 165, respectively (Fig. S1a). The largest domain (D01) covers a broad area at a coarser resolution (27 km), while the medium (D02, 9 km) and smallest (D03, 3 km) domains focus on smaller regions, with the finest resolution capturing details in our study area. The innermost domain contains both the sea and the mountains around the PRD and includes the highly-urbanized cities such as Guangzhou, Shenzhen, Dongguan, and Foshan (Fig. S1b). There were 35 vertical levels and the upper boundary was set to 50 hPa. The fifth-generation reanalysis data (ERA5) of the European Centre for Medium-Range Weather Forecasts (ECMWF, https://www.ecmwf.int/) were adopted as the initial and boundary conditions with resolution of 1 h and 0.25° (Hersbach et al., 2020). To reduce model bias, we applied the four-dimensional data assimilation (FDDA) implemented in the WRF model for all domains in all experiments (Lo et al., 2008). The FDDA analysis interval was set to 3 h, with nudging coefficients configured as 0.0003 for winds, temperature, and water vapor variables. The nudging coefficients are default values of the WRF model, which are derived from observation-based nudging studies in WRF’s predecessor models (Spero et al., 2018).

Identical physical schemes were performed on all simulations, shown in Table 1. The Morrison Two-moment scheme was chosen for cloud microphysics as it has been proven to perform TC features well (Morrison et al., 2009; Yang et al., 2018; Deng et al., 2019). We applied the Rapid Radiative Transfer Model for longwave radiation simulation as it produces fluxes and heating calculations accurately and efficiently (Mlawer et al., 1997). For the shortwave radiation scheme, the Dudhia scheme was adopted (Dudhia, 1989). We employed the Yonsei

<table><caption>**Table 1**

Overview of WRF physical schemes.</caption><thead><tr><td>Physics option</td><td>Parameterization scheme</td></tr></thead><tbody><tr><td>Cloud microphysics</td><td>Morrison Two-moment scheme (Morrison et al., 2009)</td></tr><tr><td>Longwave radiation</td><td>RRTM scheme (Mlawer et al., 1997)</td></tr><tr><td>Shortwave radiation</td><td>Dudhia scheme (Dudhia, 1989)</td></tr><tr><td>Planetary boundary layer</td><td>Yonsei University scheme (Hong et al., 2006)</td></tr><tr><td>Cumulus</td><td>Grell-3 scheme (Grell and Dévényi, 2002)</td></tr><tr><td>Land surface</td><td>Noah mosaic land surface model (Li et al., 2013)</td></tr><tr><td>Urban surface</td><td>Single-layer UCM (Chen et al., 2011)</td></tr><tr><td>Surface layer</td><td>Revised MM5 scheme (Jiménez et al., 2012)</td></tr></tbody></table>
University scheme for the planetary boundary layer due to its ability to model the vertical transfer of momentum, heat, and other substances (Hong et al., 2006). For land properties, we coupled the Noah Mosaic Land Surface Model with WRF for better generation of subgrid-scale variability (Li et al., 2013), especially for grids with diverse surface types (e.g., cities, forests, and cropland) Developed in the Noah land surface model (Chen and Dudhia, 2001), the Noah mosaic approach takes several land covers into account instead of only the dominant land cover for each grid cell. This is quite applicable for the urban environment simulations due to their high surface heterogeneities. In comparison, the Noah mosaic approach performs better in capturing the spatiotemporal patterns of surface energy balance, temperature, and precipitation than the dominant approach applied in the Noah model (Li et al., 2013). We also coupled the Single-Layer Urban Canopy Model (SLUCM) to the simulation with urban land cover to better represent the thermodynamic characteristics and land-atmosphere interactions of urban surfaces (Chen et al., 2011). The cumulus scheme Grell-3 was selected and was turned off in the innermost domain (Grell and Dévényi, 2002).

## 2.2. Experimental design

We conducted two contrast experiments with (CTRL) and without (NoUrban) urban land cover. The default MODIS 30 s land cover in both simulations was updated by the Copernicus Global Land Service Land Cover 100 m (CGLS-LC100) of epoch 2016 (Buchhorn et al., 2020a) for a more accurate representation of the urban scale (https://doi.org/10.5281/zenodo.3518026). The product is assessed with >80 % accuracy globally, within which Asia owns the highest accuracy of 83.7 % (Buchhorn et al., 2020b). According to the CGLS-LC100 product, the five dominant land categories on land in D03 are closed evergreen broadleaf forest, cropland, open unknown forest, urban/built-up, and open evergreen broadleaf forest. We reclassified the 23 land cover classes in CGLS-LC100 into 20 classes matching MODIS standards (the corresponding category relationships are shown in Table S1). The integration calculations for different relationships followed Huang et al. (2022). For consistent reference in subsequent analyses, we define the “Major Urban Area (MUA)” as the cluster of concentrated urbanized zones in the PRD’s central region, bounded by 112.7°E–114.2°E (longitude) and 22.45°N–23.5°N (latitude), which is shown in Figure S1c. For the NoUrban simulation, we removed all the urban proportions of D03 and increased the percentage of other land cover types proportionally (Fig. S1c, d). To reduce model uncertainties from initial conditions and physical sensitivity, we conducted ensemble runs for the CTRL and NoUrban by increasing or decreasing the water vapor mixing ratio of the lowest level of D03 for 3 ‰, 2 ‰, and 1 ‰ relative to the initial reference run, respectively, following Du et al. (2023) and Yang et al. (2018). The results shown are ensemble means from the seven members in CTRL and NoUrban, respectively. The simulation started at 12 UTC on July 30 and ended at 12 UTC on August 2, 2016. The first 24 h were treated as spin-up and were excluded from the following analysis.

We retrieved the observed data of Nida (2016) from the tropical cyclone database (https://tdata.typhoon.org.cn/zjljsjj.html) provided by the China Meteorological Administration (CMA) to validate model performance (Ying et al., 2014). The dataset contains the TC location, minimum pressure, and 2-min average maximum wind speed near the TC center at 6-hour intervals. The precipitation was evaluated using the China hourly Merged Precipitation Analysis (CMPA) product (Shen et al., 2014). The product merges hourly rainfall data from >30,000 stations over China and the precipitation product of Climate Precipitation Center Morphing (CMORPH) to produce the hourly rainfall at 0.1° spatial resolution. The gauge-satellite merged product reduces the systematic bias and improves the spatial correlation (Shen et al., 2014). The 2-m temperature and 10-m wind speed are validated using the China Meteorological Forcing Dataset (CMFD). The dataset merges various data sources, such as CMA station observations, TRMM satellite

precipitation data, and GLDAS data, with a spatial resolution of 0.1° and a temporal resolution of 3 h (He et al., 2020). We applied the correlation coefficient (r) and root-mean-squared-error (RMSE) as the evaluation indexes in model validation. The t-test was adopted to examine the significant difference for each grid of the two ensembles. The Shapiro-Wilk test was applied before the t-test, with approximately 90 % of grid points from the two ensembles exhibited p-values > 0.05, confirming that the data satisfied the normality assumption for applying t-test.

# 3. Results

## 3.1. Model validation

We determine the typhoon center by locating the minimum sea level pressure in D02. The simulated typhoon track aligns closely with the best-track location, nearly overlapping with only a slight delay (Fig. 1a). The mean track bias is 22.41 km over the study period, with the minimum bias of 9.33 km occurring at 18 UTC on August 1 before making landfall (Fig. 1b). After landfall, the track deviates slightly from the best track but remains consistent with the observed path. The model slightly overestimates typhoon intensity, as seen in both the maximum wind speed near the center (maximum value: 38.0 m s⁻¹ for observed and 41.6 m s⁻¹ for CTRL) and the minimum central pressure (minimum value: 965 hPa for observed and 962 hPa for CTRL). Generally, the simulation accurately reproduces similar patterns during the typhoon development (Fig. 1c). The typhoon movement speed is also simulated well, with a high temporal correlation of 0.87 between observations and the CTRL run (Fig. 1d).

The patterns of precipitation after the landfall are also well captured, as shown by the strong correlation coefficient between the observed CMPA product and simulations. Compared to CMPA data, the spatial pattern of the rainfall accumulation matches closely in the MUA (outlined in Fig. S1c based on the underlying surface characteristics) and its south (from the southern MUA border to 22.2°N) and west (from 112.°E to the western MUA border; Figs. 2a,b). The magnitudes of accumulated precipitation on the east (from the eastern MUA border to 115.°E) of MUA are overestimated. However, the results are in good agreement, with land precipitation showing spatial correlation coefficient of 0.63. The temporal correlation for hourly rain rate is relatively high, indicating the model effectively captures precipitation variability during landfall. Temporal correlation coefficients are higher in western D03 than in the east, peaking at 0.98, and all exceed 0.6 within the MUA (Fig. 2c). By further validating through average rain rates over D03 and MUA, the rainfall peaks are generated well (Fig. 2d). In D03, mean rain rates exceeded 4 mm h⁻¹ from 22 UTC on August 1 to 7 UTC on August 2 (maximum: 5.50 mm h⁻¹) after landfall, while the MUA’s peak rainfall was more intense and concentrated—occurring around 23 UTC on August 1 at 8.69 mm h⁻¹. The CTRL simulation closely reproduces these peaks and variations (temporal correlation of D03: *r* = 0.94, RMSE = 0.64 mm; MUA: *r* = 0.97, RMSE = 0.76 mm), with slightly lower maximum values (maximum: 4.75 mm h⁻¹) and MUA (maximum: 8.19 mm h⁻¹), respectively. Temporal patterns are also validated for urban-dominated grids within the MUA (*r* = 0.96, RMSE = 0.97 mm), the MUA downwind (*r* = 0.95, RMSE = 1.56 mm), and the MUA upwind (*r* = 0.88, RMSE = 1.07 mm) regions (Fig. S2). Further validation on 2-m temperature and 10-m wind speed within the MUA and its periphery also supports the strong performance of model simulation in this TC event (Fig. S3), with RMSE values below 1.29 °C and 2.13 m s⁻¹, respectively.

## 3.2. Features of urban-induced difference on Typhoon Nida (2016)

A comparison of the CTRL and NoUrban simulations shows that urban effects on typhoon tracks and overall intensity are limited. The tracks remain very close, with the largest discrepancy reaching just 8.93
<figure><img src="image_4.png" /><figcaption>**Fig. 1.** Model validation and differences between CTRL and NoUrban for TC track and intensity. (a) Observed and simulated track for Typhoon Nida (2016). The dots mark the position of the TC center. (b) 6-hourly track bias for CTRL and NoUrban. (c) Time series for maximum wind speed near the TC center and TC minimum central pressure. (d) Time series for TC movement speed.</figcaption></figure>

km (06 UTC August 1; Figs. 1a,b). Both wind speed and central pressure reveal slight intensification of TC in the CTRL simulation before landfall, with a maximum increase of 3.76 m s⁻¹ for wind speed and a maximum decrease of 1.52 hPa in central minimum pressure (Fig. 1c). Compared with the NoUrban simulation, the typhoon in the CTRL is slightly decelerated, with maximum deceleration (0.98 km h⁻¹) found after the landfall (00 UTC August 2; Fig. 1d). The precipitation after landfall in the NoUrban simulation resembles the CTRL (Fig. 3a), but rainfall differences are unevenly distributed. Statistically significant negative differences ($p$ < 0.05) are concentrated mainly in the western MUA, while significant positive differences ($p$ < 0.05) appear along the southern coast of the MUA (Fig. 3b). When focusing on the peak period, the rainfall differences display a clearer pattern of spatial clustering. During the 6 h, these differences form clearer spatial clusters: precipitation within the MUA decreases significantly ($p$ < 0.05) in CTRL, while rainfall increases by up to 46.17 mm in other areas (Fig. 3c). The enhanced rainfall is mainly concentrated at the southern edge of the MUA, covering both urban land and the sea. This uneven distribution is likely driven by the continuous movement of the TC center and the shifting rain bands. Hourly rain rate differences further confirm the slight rainfall reduction in the MUA, with peak differences reaching 0.69 mm h⁻¹ during the heaviest rainfall (Fig. 3d). The overall results suggest that the effect of urban land cover in this typhoon event is more evident in altering rainfall pattern rather than changing region average rainfall amounts.

The spatial pattern of precipitation difference evolves during different stages of the TC landfall (Fig. S4). When TC approaches the MUA, the CTRL precipitation concentrates on the urban downwind, along the southeast border of MUA, whereas NoUrban displays more widespread rainfall across the south of the MUA—creating a pattern of enhanced southern rainfall (Fig. 3c and Figs. S4a,d). Over the next three hours, as the TC center crosses the MUA, the wind field within the MUA becomes rotational. During this period, rainfall in CTRL mainly appears in the western MUA and the south of MUA (Fig. S4b). In comparison, NoUrban simulation experiences heavier rainfall in the northwestern

MUA and less in the south (Fig. S4e), generating the observed rainfall differences pattern in the western MUA (Fig. 3c). As Nida leaves the MUA, stronger rainfall is observed in the urban upwind (southeast MUA), and also small part of the downwind area in the north of the MUA in the CTRL than the NoUrban (Figs. S4c,f)—consistent with the increased rainfall observed in the eastern MUA (Fig. 3b).

### 3.3. Potential mechanisms for the divergent precipitation spatial pattern

#### 3.3.1. Thermal perturbations

Before and during the typhoon's landfall, thermal effects are observed. The positive 2-m temperature and sensible heat flux anomalies and the negative latent heat flux difference indicate the presence of UHI (Figs. 4a-c). The most pronounced differences in 2-m temperature and latent heat flux are concentrated in the southern MUA, whereas those in sensible heat flux are primarily observed in the eastern MUA. However, the UHI effect is weakened when the typhoon makes landfall. The 2-m temperature across the MUA peaks at 0.97 K before landfall (18 UTC August 1) and then steadily declines to 0.17 K (04 UTC August 2) during landfall (Fig. S5a). The 2-m temperature begins to rise again when the TC moves away from the MUA. The reduction of the UHI effect during TC landfall is further demonstrated by the temporal variations of sensible heat flux difference (Fig. S5b). Positive sensible heat flux anomaly persists before and after the TC landfall, but the magnitude decreases when the TC center passes through the MUA (from 22 UTC August 1 to 01 UTC August 2). As Nida leaves the MUA, the UHI effect gradually reappears, shown by the sensible heat flux and latent heat flux (Fig. S5b, c). Overall, the UHI effect remains relatively weak under such strong windy conditions during the landfall and, therefore fails to adequately trigger the upward motions inside the MUA. This explains why most of the enhanced precipitation does not occur within the MUA.

#### 3.3.2. Dynamical effects

Increased frictional velocity indicates the dynamic effect of enhanced urban surface roughness, with particularly noticeable impacts
<figure><img src="image_5.png" /><figcaption>**Fig. 2.** Model validation for precipitation. Accumulated rainfall of (a) observation and (b) model simulation (results from CTRL) during 12 UTC July 31–12 UTC August 2, 2016. (c) Temporal correlation coefficient distribution for each CMPA grid and the corresponding model grids during 12 UTC July 31–12 UTC August 2, 2016. (d) Time series of hourly rainfall averaged over D03 and MUA of observation and model simulation.</figcaption></figure>

in the southwest and east MUA (Fig. 4d). The urban effects on the rainfall anomalies within and at the edge of the MUA across different stages of the TC's passage can be revealed through the wind vector anomalies. During the initial stage of landfall, wind anomalies over the western MUA point northeast, suggesting that urban surface roughness decelerates airflows (Figs. 5a-c). The airflows then bifurcate from the western MUA on the upwind and converge at the southern edge of MUA. This redirection enhances water vapor convergence along the southern MUA boundary (Fig. S6a), reducing precipitation over the city while increasing rainfall downwind. As the TC moves over the MUA, cyclonic circulations developed over the northwest and southwest MUA, with airflow deceleration persisting (Figs. 5d-f). Airflows bypass the north-west MUA and converge at the western urban edge. Water vapor is transported from the northern MUA (around 113.5°E, 23.3°N) to its periphery (Fig. S6b). It converges in the mid-western MUA, reducing northwestern rainfall but increasing mid-western precipitation (around 23°N). The increased surface roughness also slightly decelerates TC movement for a short period, with CTRL's center positioned southeast of the NoUrban's center during this period (Fig. 5e). In the final stage of the TC movement, the upwind area shifts from the northern to the southern MUA. Airflows decelerate over the southeast MUA, forming cyclonic circulation within the city. Water vapor diverges in the northeastern MUA and converges in the southwestern MUA boundary (Fig. S6c), enhancing rainfall at the upwind MUA border while reducing it over

MUA (Figs. 5g-i).

Vertical profiles of time-averaged water vapor mixing ratio and composite winds further revealed the dynamic effect of the urban surface roughness on the MUA boundary during the TC landfall. As the TC approaches the MUA, northerly winds dominate the area (Figs. S7a,b). The flows above 850 hPa are lifted in the upwind, specifically between the northern urban boundary and the topographic transition zone, increasing water vapor at the boundary and over the southern MUA (Fig. 6a). As the lifted airflow bifurcates and moves downwind, this surplus moisture is transported to the southern MUA periphery—serving as the direct water vapor source for the enhanced rainfall observed in this downwind region. As the TC crosses the MUA, bifurcated northerly winds drive water vapor divergence over dense urban areas (Figs. 6b and S6b). The vertical pattern reveals the elimination of moisture and inhibition to convection, directly explaining the reduced precipitation over the MUA core. In contrast, over relatively sparse urban areas (around 113°E, 23°N), downward airflows converge and redirect moisture above 900 hPa. This vertical moisture accumulation above 900 hPa directly corresponds to the localized rainfall enhancement at the urban boundary (Figs. 5e and 6b). When the TC leaves the MUA, the southerly winds prevail (Figs. S7e,f), and updrafts redevelop along two key zones: the downwind region (northern urban boundary and topographic transition zone) and the upwind region (southern boundary and land-sea transition zone; Fig. 6c). The upward flows that extend from the
<figure><img src="image_6.png" /><figcaption>**Fig. 3.** Differences between CTRL and NoUrban simulation. (a) Accumulated rainfall of NoUrban and (b) difference in accumulated precipitation between CTRL and NoUrban during 12 UTC July 31–12 UTC August 2, 2016. (c) Difference in accumulated precipitation between CTRL and NoUrban during the peak period (21 UTC August 1–02 UTC August 2, 2016). (d) Difference in accumulated precipitation between CTRL and NoUrban during the peak period (21 UTC August 1–02 UTC August 2, 2016). The black lines in (b) and (c) represent significant differences of *p* < 0.05.</figcaption></figure>

surface to above 750 hPa, combined with enhanced water vapor, fuel convection and directly intensifies precipitation in these areas.

## 4. Discussion

By setting up contrast experiments with and without urban land cover, we explored the impact of urbanization on Typhoon Nida (2016). Urban land cover exerts limited influence on the typhoon's track and intensity in this event, consistent with previous studies (Yang et al., 2018; Ao et al., 2022; Du et al., 2023), since the track and intensity of TCs are mainly governed by large-scale environmental conditions (Wu et al., 2015; Wang and Toumi, 2021). Urban extent mainly increases spatial heterogeneity of precipitation rather than total amount across the simulated domain and the major urban area (MUA), reducing rain rates within the MUA while causing short-duration precipitation in downwind areas. This result is different from previous research that identified enhanced rainfall over the urban area (Yang et al., 2018, 2025). The difference may be due to the reduced UHI effect during landfall in our study. As the UHI effect continuously weakens during the landfall, thermal perturbations are likely too weak to trigger convection within the MUA under such strong synoptic conditions, as noted by Wu et al. (2024). A study from North America suggests that urban effect is relatively weak for storms with lower accumulated precipitation (Islam

et al., 2023), which provides another possible explanation for the spatiotemporal characteristics in our study.

Due to the continuously changing TC center and wind fields during landfall, urban-induced precipitation differences varies as TC goes through the MUA. Short-duration rainfall enhancement is concentrated mainly in the MUA downwind (from its southern border to 22.2°N) when the typhoon approaches the MUA, confined to the southeast MUA boundary. As the TC center moves into the MUA, enhanced rainfall shifted to the western sparse urban area, coinciding with reduced rainfall in dense urban regions. This aligns with the study in Europe that cities block the airflow and lead airflows cyclonically moving around the city, enhanced convergence in the suburb (Yang et al., 2021b). After the TC leaves the MUA, enhanced rainfall occurs at the eastern boundary of the MUA and both upwind (from the southern border to 22.2°N) and downwind (around 114°E at the northern border) regions. Increased surface roughness is believed to dominate the divergent precipitation patterns, aligning with findings from other studies (Ao et al., 2022; Du et al., 2023; Wu et al., 2024). Increased frictional velocity in urban areas decelerates airflows, causing winds to bifurcate around the city. Water vapor is then transported from the MUA and converges in downwind or upwind regions. This matches the “Warm-Windy” scenario summarized by Shen and Yang (2023), where enhanced precipitation occurs outside the city under weak UHI and strong flow conditions. Additionally,
<figure><img src="image_7.png" /><figcaption>**Fig. 4.** UHI effects and frictional velocity. CTRL-minus-NoUrban differences on (a) 2-m temperature (T2), (b) latent heat (LH), (c) sensible heat (SH), and (d) friction velocity (FV) between 18 UTC 01 August and 06 UTC 02 August. Dashed lines denote the MUA.</figcaption></figure>

airflow interacting with the urban canopy generated more updrafts at the urban boundary, lifting more water vapor to higher levels and intensifying precipitation, which is consistent with previous studies (Zhang et al., 2018; Islam et al., 2023).

Beyond the MUA and its periphery, updrafts and increased water vapor are observed over topography transition area and land-sea transition area, indicating interactions between urban effects and local factors. Studies on North America and Argentina have confirmed that urban-topography interactions can trigger convergence and amplify rainfall (Freitag et al., 2018; Yang et al., 2019)—a mechanism that may also applicable in the PRD. Gao et al. (2021) have revealed their combined impact in initiating convergence during the 2017 extreme rainfall event in Guangzhou. Meanwhile, cities near open water experience enhanced moisture and unstable air plumes that synergistically intensify convection, bringing more precipitation to the urban area (Xing et al., 2019). In the case study of coastal precipitation in Houston, urban-sea breeze interactions amplify convective forcing and cloud development (Shepherd et al., 2010). The sea breeze can also be regulated through the enhancement of sensible heat from urban areas (Ryu et al., 2016). Given the PRD's surrounding mountains and proximity to the South China Sea, the precipitation pattern in the PRD may be further shaped by urban-sea interaction and urban-topography interaction. This needs additional sensitivity experiments, which is beyond the scope of the present study.

The study highlights the importance of urban land cover in altering TC rainfall patterns. While our analysis focuses on Typhoon Nida (2016)

in the Pearl River Delta, the core mechanisms identified offer broader insights applicable to other TC-prone coastal urban regions. Future work can be conducted by analyzing multiple TCs across coastal megacities, which enables the identification of generalizability. These changes could increase the risk of urban flash floods and widespread hazards over the metropolitan area in a short period. The high spatial rainfall heterogeneity challenges traditional flood mitigation strategies that often focus on known hotspots within MUA. The rapid shift in enhanced rainfall as the typhoon traverses the urban area also challenges early flood warning systems and emergency responses. Our finding raises concerns about the increasing socio-economic damage from short-term heavy rainfall and high rainfall spatial heterogeneity in the urban areas, especially for coastal cities with growing populations and frequent tropical cyclones. Given the fact that future intensified TCs and urban expansion could produce more extreme precipitation over the urban areas through multiple mechanisms (Sui et al., 2024), precipitation variability is likely to increase. Future work can build on the findings of this study by integrating typhoon precipitation redistribution patterns with urban hydrological models and fine-scale exposure data to enable quantitative assessment of flood risk differences across urban zones. Adaptive urban planning that accounts for TC-induced rainfall redistribution could be effective. For example, widespread green infrastructure planning may increase urban resilience by reducing runoff and absorbing rainwater into vegetation and soils (Wang et al., 2024).

There are still uncertainties regarding the absence of the urban
<figure><img src="image_8.png" /><figcaption>**Fig. 5.** CTRL-minus-NoUrban differences of precipitation and 900 hPa wind field between 21 UTC August 1–05 UTC August 2, 2016. The red crossing denotes the typhoon center in CTRL and the red plus denotes the typhoon center in NoUrban at the time. The black dashed lines outline the MUA and the red dashed lines outline the cross-section area where the vertical structure is investigated later in Fig. 6. The pink arrows point out examples of bifurcated wind direction.</figcaption></figure>

aerosol effect and the deficiency in model parameterizations as the joint effect of urban aerosol and urban land may contribute to different stages of precipitation development (Fan et al., 2020). However, previous studies have noted that urban aerosol effect is less pronounced than surface roughness influence in altering TC precipitation during strong synoptic forcing (Zhong et al., 2017; Yang et al., 2025). Even so, the absence of urban aerosols may introduce underestimation in total rainfall since aerosols tend to enhance the ice-phase process (Yang et al., 2025). Additional analysis tropical cyclone structure and landfall

mechanisms can provide further detailed assessments of the impacts of local factors on such events.

## 5. Conclusion

To investigate the effect of urbanization of the Pearl River Delta on the landfall process of Typhoon Nida (2016), we designed two sets of simulations using the WRF model. The influence of urbanization on the typhoon track and intensity is subtle. The role of urban land cover in
<figure><img src="image_9.png" /><figcaption>**Fig. 6.** Averaged vertical profiles. 3-hour-averaging vertical profiles of CTRL-minus-NoUrban differences between (a) 21–23 UTC (averaged within the area shown in Fig. 5a), (b) 00–02 UTC (averaged within the area shown in Fig. 5d), and (c) 02–05 UTC (averaged within the area shown in Fig. 5g) from 01 August to 02 August, averaged in latitude. Shading represents water vapor mixing ratio differences (g kg⁻¹) and vectors denote composite zonal and vertical winds (vertical wind multiplied by 10). Dashed lines represent the MUA.</figcaption></figure>

shaping landfall precipitation is more evident in altering spatial patterns rather than changing region-averaged rain rates, which remain nearly unchanged. There is mostly a reduction within the major urban area (MUA), with short-duration rainfall enhancement observed in the southern downwind MUA during peak periods. As the TC center passes through the urban area, increased urban surface roughness, combined with a weakened urban heat island effect, redirects airflows around densely populated urban zones, transporting water vapor and causing convergence downwind. Due to the rapid TC movement, the wind redirection leads to spatially rainfall redistribution, contributed to precipitation enhancement in multiple locations in a short period. The study warrants the risks of short-time multi-location flash floods across large parts of metropolitan regions during TC landfall, providing further insights into urban hazard mitigation strategies.

CRediT authorship contribution statement

**Xiaowen Huang:** Writing – review & editing, Writing – original draft, Visualization, Validation, Methodology, Investigation, Formal analysis. **Long Yang:** Writing – review & editing, Supervision, Methodology. **Laurent Z.X. Li:** Writing – review & editing, Supervision. **Dashan Wang:** Supervision, Methodology. **Maofeng Liu:** Writing – review & editing, Supervision. **Yibing Su:** Writing – review & editing, Supervision. **Zhenzhong Zeng:** Writing – review & editing, Supervision, Methodology, Funding acquisition, Conceptualization.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
## Data availability statement

The source code for WRF-ARW model (Version 4.2.2) can be retrieved from https://github.com/wrf-model/WRF/releases. The ERA5 dataset is available at https://cds.climate.copernicus.eu/cdsapp#!/home. The land cover product, CGLS-LC100 is available at https://doi.org/10.5281/zenodo.3518026. The observational tropical cyclone best track precipitation data can be accessed at https://tcdata.typhoon.org.cn/zjljsjj.html. The CMFD dataset can be obtained at http://poles.tpdc.ac.cn/zh-hans/data/8028b944-daaa-4511-8769-965612652c49/. The CMPA dataset are not currently available publicly for legal reasons but can be retrieved from the corresponding author on reasonable request.

## Acknowledgments

This study was supported by the National Natural Science Foundation of China (42071022), Shenzhen Science and Technology Project for Sustainable Development in Special Innovation (KCXFZ20230731093403008), Guangdong Basic and Applied Basic Research Foundation (no. 2022A1515240070), the start-up fund provided by the Southern University of Science and Technology (29/Y01296122), High level of special funds (G030290001), and the Shenzhen Key Laboratory of Precision Measurement and Early Warning Technology for Urban Environmental Health Risks (ZD SYS20220606100604008). We thank the Center for Computational Science and Engineering at the Southern University of Science and Technology for providing computing resources. Z.Z. thanks Otto Poon Research Institute for Climate-Resilient Infrastructure at the Hong Kong Polytechnic University for providing the visiting fellowship. L. Y. is supported by the Basic Research Program of Jiangsu Province (Grant No BK20231541).

## Supplementary materials

Supplementary material associated with this article can be found, in the online version, at doi:10.1016/j.horiz.2025.100160.

## References

Ao, X., Yue, C., Yang, X., Deng, L., Huang, W., 2022. Urbanization effects on rainfall processes induced by landfalling Typhoon Lekima (2019) over the Shanghai Metropolitan Area. J. Hydrometeorol. 23, 1075–1093. https://doi.org/10.1175/JHM-D-21-0170.1

Argüeso, D., Di Luca, A., Evans, J.P., 2016. Precipitation over urban areas in the western Maritime Continent using a convection-permitting model. Clim. Dyn. 47, 1143–1159. https://doi.org/10.1007/s00382-015-2893-6

Buchhorn, M., Smets, B., Bertels, L., Roo, B.D., Lesiv, M., Tsendbazar, N.E., Herold, M., Fritz, S., 2020a. Copernicus Global Land Service: land Cover 100m: collection 3: epoch 2016: globe (V3.0.1). Zenodo. https://doi.org/10.5281/zenodo.3518026.

Buchhorn, M., Smets, B., Bertels, L., Roo, B.D., Lesiv, M., Tsendbazar, N.E., Li, L., Tarko, A., 2020b. Copernicus global land service: land cover 100m: version 3 globe 2015-2019: product user manual. Zenodo. https://doi.org/10.5281/zenodo.3938963.

Chang, C., Lei, Y., Sui, C., Lin, X., Ren, F., 2012. Tropical cyclone and extreme rainfall trends in East Asian summer monsoon since mid-20th century. Geophys. Res. Lett. 39. https://doi.org/10.1029/2012GL052945. L18702.

Chang, C., Yang, Y., Kuo, H., 2013. Large increasing trend of tropical cyclone rainfall in Taiwan and the roles of terrain. J. Clim. 26, 4138–4147. https://doi.org/10.1175/JCLI-D-12-00463.1

Chen, L., Li, Y., Cheng, Z., 2010. An overview of research and forecasting on rainfall associated with landfalling tropical cyclones. Adv. Atmos. Sci. 27, 967–976. https://doi.org/10.1007/s00376-010-8171-y

Chen, F., Dudhia, J., 2001. Coupling an advanced land surface–hydrology model with the Penn State–NCAR MM5 modeling system. Part I: model implementation and sensitivity. Mon. Weather Rev. 129, 569–585. https://doi.org/10.1175/1520-0493 (2001)129<0569. CAALSH>2.0.CO;2

Chen, F., et al., 2011. The integrated WRF/urban modelling system: development, evaluation, and applications to urban environmental problems. Int. J. Climatol. 31, 273–288. https://doi.org/10.1002/joc.2158.

Deng, L., Gao, W., Duan, Y., 2019. A modeling study of the effects of vertical wind shear on the raindrop size distribution in Typhoon Nida (2016). J. Geophys. Res.: Atmos. 124, 6501–6517. https://doi.org/10.1029/2018JD029946.

Dudhia, J., 1989. Numerical study of convection observed during the winter monsoon experiment using a mesoscale two-dimensional model. J. Atmos. Sci. 46, 3077–3107. https://doi.org/10.1175/1520-0469(1989)046.<3077:NSOCOD>2.0.CO;2

Du, X., Chen, H., Li, Q., Ge, X., 2023. Urban impact on landfalling tropical cyclone precipitation: a numerical study of Typhoon Rumbia (2018). Adv. Atmos. Sci. 40, 988–1004. https://doi.org/10.1007/s00376-022-2100-8

Fan, J., Zhang, Y., Li, Z., Hu, J., Rosenfeld, D., 2020. Urbanization-induced land and aerosol impacts on sea-breeze circulation and convective precipitation. Atmos. Chem. Phys. 20, 14163–14182. https://doi.org/10.5194/acp-20-14163-2020

Freitag, B.M., Nair, U.S., Niyogi, D., 2018. Urban modification of convection and rainfall in complex terrain. Geophys. Res. Lett. 45, 2507–2515. https://doi.org/10.1002/2017GL076834.

Fudeyasu, H., Hirose, S., Yoshioka, H., Kumazawa, R., Yamasaki, S., 2014. A global view of the landfall characteristics of tropical cyclones. Tropical Cyclone Res. Rev. 3, 178–192. https://doi.org/10.6057/2014TCR03.04.

Gao, Z., Zhu, J., Guo, Y., Luo, N., Fu, Y., Wang, T., 2021. Impact of land surface processes on a record-breaking rainfall event on May 06–07, 2017, in Guangzhou. China. J. Geophys. Res.: Atmos. 126. https://doi.org/10.1029/2020JD032997 e2020JD032997.

Grell, G.A., Dévenyi, D., 2002. A generalized approach to parameterizing convection combining ensemble and data assimilation techniques. Geophys. Res. Lett. 29. https://doi.org/10.1029/2002GL015311, 38-1-38-4.

Han, J.Y., Baik, J.J., 2008. A theoretical and numerical study of urban heat island-induced circulation and convection. J. Atmos. Sci. 65, 1859–1877. https://doi.org/10.1175/2007JAS2326.1

He, L., Li, Q., Wang, Y., Lee, J.H.W., Xu, Y., Li, G., 2023. Effects of urban expansion and anthropogenic heat enhancement on tropical cyclone precipitation in the Greater Bay Area of China. J. Geophys. Res.: Atmos. 128. https://doi.org/10.1029/2022JD038184 e2022JD038184.

He, J., Yang, K., Tang, W., Lu, H., Qin, J., Chen, Y., Li, X., 2020. The first high-resolution meteorological forcing dataset for land process studies over China. Sci. Data 7. https://doi.org/10.1038/s41597-020-0369-y.

Hersbach, H., et al., 2020. The ERA5 global reanalysis. Q. J. R. Meteorol. Soc. 146, 1999–2049. https://doi.org/10.1002/qj.3803.

Hong, S.Y., Noh, Y., Dudhia, J., 2006. A new vertical diffusion package with an explicit treatment of entrainment processes. Mon. Weather Rev. 134, 2318–2341. https://doi.org/10.1175/MWR3199.1

Hu, L., et al., 2023. Economic impacts of tropical cyclone-induced multiple hazards in China. Earths Future 11. https://doi.org/10.1029/2023EF003622 e2023EF003622.

Huang, X., Wang, D., Li, L.Z.X., Li, Q., Zeng, Z., 2022. Modeling urban impact on Zhengzhou storm on July 20, 2021. J. Geophys. Res.: Atmos. 127. https://doi.org/10.1029/2022JD037387 e2022JD037387.

Iossifova, D., 2019. East Asian Urbanization. *The Wiley Blackwell Encyclopedia of Urban and Regional Studies* Ed Orum A. M. John Wiley & Sons, pp. 1–10. https://doi.org/10.1002/9781118568446.eurs0084.

Islam, S., Villarini, G., Zhang, W., 2023. Quantification of the role of urbanization in changing the rainfall associated with tropical cyclones affecting Charlotte. North Carolina. Urban Clim 52, 101681. https://doi.org/10.1016/j.uclim.2023.101681.

Jiménez, P.A., Dudhia, J., González-Rouco, J.F., Navarro, J., Montávez, J.P., García-Bustamante, E., 2012. A revised scheme for the WRF surface layer formulation. Mon. Weather Rev. 140, 898–918. https://doi.org/10.1175/MWR-D-11-00056.1

Li, D., Bou-Zeid, E., Barlage, M., Chen, F., Smith, J.A., 2013. Development and evaluation of a mosaic approach in the WRF-Noah framework. J. Geophys. Res.: Atmos. 118, 11918–11935. https://doi.org/10.1002/2013JD020657.

Li, Y., Zhao, S., Zhao, D., Gao, G., Xu, H., Jiang, Y., 2023. Changes in tropical cyclone disasters over China during 2001–2020. Earth Space Sci 10. https://doi.org/10.1029/2022EA00279 e2022EA00279.

Lo, J.C.F., Yang, Z., Pielke Sr, R.A., 2008. Assessment of three dynamical climate downscaling methods using the Weather Research and Forecasting (WRF) model. J. Geophys. Res.: Atmos. 113. https://doi.org/10.1029/2007JD009216. D09112.

Mlawer, E.J., Taubman, S.J., Brown, P.D., Iacono, M.J., Clough, S.A., 1997. Radiative transfer for inhomogeneous atmospheres: RRTM, a validated correlated-k model for the longwave. J. Geophys. Res. 102, 16663–16682. https://doi.org/10.1029/97JD00237.

Mori, N., Takemi, T., Tachikawa, Y., Tatano, H., Shimura, T., Tanaka, T., Fujimi, T., Osakada, Y., Webb, A., Nakakita, E., 2021. Recent nationwide climate change impact assessments of natural hazards in Japan and East Asia. Weather Clim. Extrem. 32, 100309. https://doi.org/10.1016/j.wace.2021.100309.

Morrison, H., Thompson, G., Tatarskii, V., 2009. Impact of cloud microphysics on the development of trailing stratiform precipitation in a simulated squall line: comparison of one-and two-moment schemes. Mon. Weather Rev. 137, 991–1007. https://doi.org/10.1175/2008MWR2556.1

Park, D.S.R., Ho, C.H., Kim, J.H., 2014. Growing threat of intense tropical cyclones to East Asia over the period 1977–2010. Environ. Res. Lett. 9, 014008. https://doi.org/10.1088/1748-9326/9/1/014008.

Qiu, W., Ren, F., Wu, L., Chen, L., Ding, C., 2019. Characteristics of tropical cyclone extreme precipitation and its preliminary causes in Southeast China. Meteorol. Atmos. Phys. 131, 613–626. https://doi.org/10.1007/s00703-018-0594-5.

Rosenfeld, D., Lohmann, U., Raga, G.B., O'Dowd, C.D., Kulmala, M., Fuzzi, S., Reissell, A., Andreae, M.O., 2008. Flood or drought: how do aerosols affect precipitation? Science 321, 1309–1313. https://doi.org/10.1126/science.1160606.

Ryu, Y.H., Smith, J.A., Bou-Zeid, E., Baeck, M.L., 2016. The influence of land surface heterogeneities on heavy convective rainfall in the Baltimore-Washington metropolitan area. Mon. Weather Rev. 144, 553–573. https://doi.org/10.1175/MWR-D-15-0192.1
Shem, W., Shepherd, M., 2009. On the impact of urbanization on summertime thunderstorms in Atlanta: two numerical model case studies. Atmos. Res. 92, 172–189. https://doi.org/10.1016/j.atmosres.2008.09.013.

Shen, Y., Zhao, P., Pan, Y., Yu, J., 2014. A high spatiotemporal gauge-satellite merged precipitation analysis over China. J. Geophys. Res.: Atmos. 119, 3063–3075. https://doi.org/10.1002/2013JD020686.

Shen, L., Wu, C., Judt, F., 2021. The role of surface heat fluxes on the size of Typhoon Megi (2016). J. Atmos. Sci. 78, 1075–1093. https://doi.org/10.1175/JAS-D-20-0141.1.

Shen, Y., Yang, L., 2023. Divergent urban signatures in rainfall anomalies explained by pre-storm environment contrast. Geophys. Res. Lett. 50. https://doi.org/10.1029/2022GL010658 e2022GL010658.

Shepherd, J.M., Carter, M., Manyin, M., Messen, D., Burian, S., 2010. The impact of urbanization on current and future coastal precipitation: a case study for Houston. Environ. Plann. B 37, 284–304. https://doi.org/10.1068/b34102t.

Skamarock, W.C., et al., 2019. A description of the advanced research WRF version 4. NCAR tech note ncar/tn-556+ str 145.

Smith, R.B., Schafer, P., Kirshbaum, D., Regina, E., 2009. Orographic enhancement of precipitation inside Hurricane Dean. J. Hydrometeorol. 10, 820–831. https://doi.org/10.1175/2008JHM1057.1.

Spero, T.L., Nolte, C.G., Mallard, M.S., Bowden, J.H., 2018. A maieutic exploration of nudging strategies for regional climate applications using the WRF Model. J. Appl. Meteorol. Climatol. 57, 1883–1906. https://doi.org/10.1175/jamc-d-17-0360.1.

Sui, X., Yang, Z., Shepherd, M., Niyogi, D., 2024. Global scale assessment of urban precipitation anomalies. Proc. Natl. Acad. Sci. U.S.A. 121, e2311496121. https://doi.org/10.1073/pnas.2311496121.

Utsumi, N., Kim, H., Kanae, S., Oki, T., 2017. Relative contributions of weather systems to mean and extreme global precipitation. J. Geophys. Res.: Atmos. 122, 152–167. https://doi.org/10.1002/2016JD025222.

Utsumi, N., Kim, H., 2022. Observed influence of anthropogenic climate change on tropical cyclone heavy rainfall. Nat. Clim. Change 12, 436–440. https://doi.org/10.1038/s41558-022-01344-2.

Wang, D., Wang, X., Liu, L., Wang, D., Huang, H., Pan, C., 2016. Evaluation of CMPA precipitation estimate in the evolution of typhoon-related storm rainfall in Guangdong. China. J. Hydroinform. 18, 1055–1068. https://doi.org/10.2166/hydro.2016.241.

Wang, J., Li, Z., Tang, L., Yang, S., 2018a. Characteristics of landfalling typhoon Nida (1604) and analysis of its causation. Guangdong Meteorol. 40, 10–14. https://doi.org/10.3969/j.issn.1007-6190.2018.05.003.

Wang, J., Feng, J., Yan, Z., 2018b. Impact of extensive urbanization on summertime rainfall in the Beijing region and the role of local precipitation recycling. J. Geophys. Res.: Atmos. 123, 3323–3340. https://doi.org/10.1002/2017JD027725.

Wang, S., Toumi, R., 2021. Recent migration of tropical cyclones toward coasts. Science 371, 514–517. https://doi.org/10.1126/science.abb9038.

Wang, Z., Li, Z., Wang, Y., Zheng, X., Deng, X., 2024. Building green infrastructure for mitigating urban flood risk in Beijing. China. Urban For. Urban Green 93, 128218. https://doi.org/10.1016/j.ufug.2024.128218.

Wu, L., Su, H., Fovell, R.G., Dunkerton, T.J., Wang, Z., Kahn, B.H., 2015. Impact of environmental moisture on tropical cyclone intensification. Atmos. Chem. Phys. 15, 14041–14053. https://doi.org/10.5194/acp-15-14041-2015.

Wu, J., Gao, L., Meng, Q., Wang, H., 2024. Effect of land cover pattern on rainfall during a landfalling typhoon: a simulation of Typhoon Hato. Atmos. Res. 303, 107329. https://doi.org/10.1016/j.atmosres.2024.107329.

Xing, Y., Ni, G., Yang, L., Yang, Y., Xing, P., Sun, T., 2019. Modeling the impacts of urbanization and open water surface on heavy convective rainfall: a case study over the emerging Xiong'an City. China. J. Geophys. Res.: Atmos. 124, 9078–9098. https://doi.org/10.1029/2019JD030359.

Yang, L., Smith, J., Niyogi, D., 2019. Urban impacts on extreme monsoon rainfall and flooding in complex terrain. Geophys. Res. Lett. 46, 5918–5927. https://doi.org/10.1029/2019GL083363.

Yang, L., Li, Q., Yuan, H., Niu, Z., Wang, L., 2021a. Impacts of urban canopy on two convective storms with contrasting synoptic conditions over Nanjing. China. J. Geophys. Res.: Atmos. 126. https://doi.org/10.1029/2020JD034509 e2020JD034509.

Yang, L., Ni, G., Tian, F., Niyogi, D., 2021b. Urbanization exacerbated rainfall over european suburbs under a warming climate. Geophys. Res. Lett. 48. https://doi.org/10.1029/2021GL095987 e2021GL095987.

Yang, T., Duan, Y., Xu, J., Feng, J., 2018. Simulation of the urbanization impact on precipitation of landfalling tropical cyclone Nida (2016). J. App. Meteorological Sci. 29, 410–422. https://doi.org/10.11898/1001-7313.20180403.

Yang, Y., Chan, J.C., Zhao, K., Wang, S., 2025. Megacity effects on rainfall induced by Typhoon Nida. Commun. Earth Environ. 6, 388. https://doi.org/10.1038/s43247-025-02373-3.

Ying, M., Zhang, W., Yu, H., Lu, X., Feng, J., Fan, Y., Zhu, Y., Chen, D., 2014. An overview of the China Meteorological Administration tropical cyclone database. J. Atmos. Ocean. Technol. 31, 287–301. https://doi.org/10.1175/JTECH-D-12-00119.1.

Zhang, W., Villarini, G., Vecchi, G.A., Smith, J.A., 2018. Urbanization exacerbated the rainfall and flooding caused by hurricane Harvey in Houston. Nature 563, 384–388. https://doi.org/10.1038/s41586-018-0676-z.

Zhong, S., Qian, Y., Zhao, C., Leung, R., Wang, H., Yang, B., Fan, J., Yan, H., Yang, X.Q., Liu, D., 2017. Urbanization-induced urban heat island and aerosol effects on climate extremes in the Yangtze River Delta region of China. Atmos. Chem. Phys. 17, 5439–5457. https://doi.org/10.5194/acp-17-5439-2017.

<figure><img src="image_10.png" /></figure>

Zhenzhong Zeng is a Professor at Southern University of Science and Technology in Shenzhen, China. He is mainly engaged in research in the fields of ecological hydrology, land-atmosphere interaction, global environmental change and earth system simulation. He is committed to understanding the dynamic mechanism of the earth system and realizing the sustainable development of human society.