Global stalled tropical cyclones in a changing climate

Domain: biology
Published: 2026-03-30
Editorial outcome: major_revision

Abstract
Tropical cyclone (TC) stalling refers to a storm wandering within a relatively small region. When
TC stalling occurs, localized accumulated damage can increase substantially. However, the
understanding of this special behavior globally, especially its response to climate warming,
remains limited. Here, we provide a comprehensive global analysis of TC stalling and its response
to climate warming, utilizing both observational data and climate model simulations. Our results
reveal a distinct hemispheric asymmetry, showing that basins in the Southern Hemisphere are more
prone to TC stalling than those in the Northern Hemisphere. Although a warming climate reduces
the global probability of TC stalling occurrence, it significantly increases the daily rainfall by these
storms, particularly over land and nearshore regions. Our analysis also indicates that, although the
main drivers for the stalling vary in different basins, in general, they are mainly influenced by the
steering wind vector (magnitude and direction) and TC location. Furthermore, changes in
probability of TC stalling in climate warming are mainly affected by changes in the probability of
TC exposure to a weak steering flow.

Introduction
The motion of tropical cyclones (TCs) is a key factor in determining the location and extent
of storm-related damages1,2. For a specific location, slow-moving TCs prolong the exposure to the
storms, leading to greater accumulated damage. Thus, it is crucial to better understand how the
motion of TCs may change under climate change.

TC stalling, which refers to a storm wandering within a relatively small region for a prolonged
period due to its slow translation speed and/or large turning angle, is an important behavior of TC
motion3,4. Hurricane Harvey (2017) exemplifies this behavior, with its re-curvature near Houston,
Texas, where it remained for nearly three days, resulting in accumulated rainfall exceeding 1270
mm in five days5,6, more than 95% of the annual mean rainfall of 1317 mm per year over this
region. This event caused unprecedented flooding and is one of the costliest natural disasters in
U.S. history. Events like TC Ewiniar (2018), which caused record-breaking rainfall while stalling
along the South China coast7, further highlight the role of stalling in amplifying the destructive
potential of TCs.

Recent studies have focused on TC stalling in various cyclone-prone regions. TC stalling near
the North American coast was found to have been increasing between 1944 and 2017, potentially
associated with low-frequency natural variability4. Another study outlined the spatiotemporal
climatology of stalled TCs across the North Atlantic (NA), revealing their patterns of seasonality
and rapid intensification8. In parts of the Western North Pacific (WP), TC stalling has shifted
poleward during the 1979-2020 period, with results suggesting that binary cyclone interactions
may play a role in triggering these stalled events3. Despite these regional insights, a global
perspective on TC stalling remains limited.

These observed changes in the historical record have been happening in a background
characterized by human-induced warming. Recent studies indicate that climate change is expected
to intensify different storm characteristics, including the average TC intensity, the TC rapid
intensification, the proportion of strong TCs (1-min maximum sustained winds (MSW) of at least
113 kt), the average TC rainfall rates, and the frequency of TC induced heavy rainfall9–15.
Additionally, while observational evidence remains inconclusive regarding the role of
anthropogenic warming on the global TC frequency and mean translation speed9,16–19, numerical
simulations indicate that climate change has reduced the global frequency of TCs and is projected
to increase their future annual-mean translation speed20–23. Despite substantial progress in
understanding the impacts of climate change on the aforementioned TC characteristics, it is still
unclear how TC stalling responds to climate warming.

Here, we provide a global perspective on the climatology of TC stalling based on
observational data and investigate how the frequency of TC stalling and its associated rainfall may
change under climate change. Moreover, we examine the potential dynamical factors inducing TC
stalling and investigate the primary drivers responsible for its variability in a warming climate.
Our results reveal a distinct hemispheric asymmetry, showing that basins in the Southern
Hemisphere are more prone to TC stalling than those in the Northern Hemisphere. Although a
warming climate reduces the global probability of TC stalling occurrence, it significantly increases
the daily rainfall by these storms, particularly over land and nearshore regions. Our analysis also
indicates that, although the main drivers for the stalling vary in different basins, they are mainly
influenced by the steering wind vector (magnitude and direction) and TC location. Furthermore,
changes in probability of TC stalling in climate warming are mainly affected by changes in the
probability of TC exposure to a weak steering flow.

Results
Global climatology of TC stalling

Here, we defined the TC stalling as the behavior of a TC remaining within a 200-km radius
for more than 48 hours. During the 1982-2019 period, stalling occurs in ~28% of the tracked TC
centers of circulation worldwide, resulting in a total of 1626 stalled segments; 44% of TCs
experience stalling, and about 6% contain two or more stalling segments. Over land and nearshore
regions (i.e., areas within 200km of the coastline), stalling occurs in ~36% of TC track points,
leading to 353 stalled TC segments (see Methods). Fig. 1E shows the general characteristics of
global stalled and non-stalled TC track points from 1982-2019. In terms of TC motion, stalled
track points have lower translation speeds and greater turning angles compared to non-stalled
points, features consistent with the definition of TC stalling. Specifically, the median translation
speed of stalled track points was 8.2 km h−1, less than half of that of non-stalled track points (18.4
km h−1). The median turning angle of stalled track points was 12.1° h−1, more than double that of
non-stalled track points (5.6° h−1). Regarding TC intensity, density estimates at high MSW for non-
stalled track points are higher than those for stalled track points on a global scale, with this
difference mainly occurring in the Northern Hemisphere (NH) (Supplementary Fig. 1-3). The ratio
of TC stalling frequency to total TC activity (hereafter referred to as TC stalling ratio) of the
Southern Hemisphere (SH) is 1.69 times that of the Northern Hemisphere (Fig. 1c-d and
Supplementary Table 1), with the lowest TC stalling ratio observed in the NA basin and the highest
in the South Pacific (SP) basin (Supplementary Fig. 1-3c).

Regarding TC rainfall, our results indicate that TC stalling generally produces more intense
rainfall over a smaller area compared to non-stalled tracks (see the “TC rainfall” section in the
Methods; Fig. 1f). Globally, on a rolling 24-hour timescale, stalling leads to ~12% more
accumulated rainfall within an area ~15% smaller than that of non-stalled tracks. However, the
Eastern Pacific (EP) basin is an exception, where the accumulated rainfall from TC stalling is
smaller than that from non-stalled storms. This can be attributed to the relatively lower sea surface
temperature (SST) in the EP basin compared to that in other basins. Specifically, TC rainfall area
is controlled primarily by its surrounding SST relative to the tropical mean SST (that is, the relative
SST)24. This low SST in the EP basin results in small rainfall area of TCs and overlapping area
(Supplementary Fig. 2f), thereby reducing the local rainfall accumulation induced by TC stalling.
On the other hand, there is a smoother distribution of overlap ratio (see Methods) with a lower
median for stalled points in the EP compared to the other basins, which may partially explain the
rainfall characteristics in this region. Nonetheless, the extreme rolling 24-hour rainfall values (i.e.,
above the 95th percentile of the rainfall distribution at a given location) of TC stalling are greater
than those of non-stalled tracks in all basins, suggesting that TC stalling poses a greater hydrologic
threat.

Fig. 2 shows the spatial pattern of the frequency and rainfall associated with TC stalling.
Regions with high absolute frequency of TC stalling overlap with areas that experience large TC
activity, so we focus on the TC stalling ratio. Although numerous TC stalling events occur in the
South China Sea and Philippine Sea in the WP, as well as the EP off the coast of Mexico, the
corresponding TC stalling ratios are only ~10% to 20%. In contrast, the average TC stalling ratio
in the low-latitude regions of the SH (i.e., 4° to 16°S) and the land and offshore regions of the
Northern Indian (NI) exceed 35%, indicating that TC stalling is more likely to occur in these
regions. Similar spatial patterns are obtained from the analyses of annual rainfall. However, due
to a lack of coverage from geostationary satellites over an area centered on 70° east during the July
1983-June 1998 (except for April 1988-March 1989), there is an artifact in the observations over
the NI during this period, commonly called the “Indian Ocean gap”25, and the results of this basin
need to be interpreted with caution.

Climate change impact on TC stalling

Our high-resolution ensemble simulations reproduce the spatial and temporal patterns of the
observed frequency and rainfall of TC stalling, providing confidence in exploring the impacts of
climate warming on TC stalling (Fig. 2e-h and Supplementary Fig. 4-7) (see Method for more
details on the validation). Therefore, we analyze the impact of climate change on TC stalling
frequency and daily rainfall by comparing ensemble simulations under different scenarios (see
Methods). Although climate change over the 20th century has led to a non-significant global
decrease (-2.1%) in the TC stalling ratio, this decrease is projected to become more pronounced
with increasing temperatures throughout the 21st century (-7.2% to -4.1%; Fig. 3a). On a basin-
wide scale, climate change has already significantly reduced the TC stalling ratios in the WP (-
3.2%) and NA basins (-13.0%), while it has increased the stalling ratio in the SI basin (~4.3%).
Compared to the other basins, only the WP basin shows significant negative changes in the TC
stalling ratio due to climate change over the 20th century and for future conditions. Except for the
EP and NA basins, TC stalling ratios are projected to decrease in most basins, even though these
changes are not significant. Over land and nearshore regions, global warming has not significantly
altered TC stalling ratios either globally or within individual basins during the historical period but
is projected to significantly reduce the TC stalling ratio in the WP basin between -3.7% and -6.8%
(Fig. 3b). A previous study4 reports an increasing trend in near-coastal stalling events in the NA
basin, different from our results; this difference is primarily attributable to differences in the spatial
extent of the study areas: our analysis encompasses a broader domain covering all the land and
nearshore regions in the NA basin, while their investigation focuses on the coastline of the
American continent within the NA basin (see Fig. 2 in Ref. 4). From a spatial perspective, climate
change results in extensive, contiguous regions with decreased TC stalling ratios in the WP basin
(Fig. 3e-j). In the South China Sea, Caribbean Sea, and Gulf of Mexico, the spatial patterns of
changes in TC stalling ratio exhibit roughly similar characteristics between the “Near Future” and
“Far Future” scenarios (see Method for more details on the numerical experiments). In the low-
latitude SI basin, climate warming has increased TC stalling ratios in most of the region, whereas
projections point to a change in the opposite direction, similar to what found in the NA basin,
excluding the Caribbean Sea and Gulf of Mexico.

Regarding rainfall intensity, climate change has resulted in a significant increase in daily
rainfall associated with TC stalling globally (3.8%), and these increases are further amplified with
future warming across all basins (~5.2-10.1%) (Fig. 3c). Among them, only TC stalling-induced
rainfall in the Pacific Ocean shows a significant positive response to climate change over the 20th
century, indicating that it is possible to already detect a global warming signal in the historical
record for that basin. Spatially, there is a larger and more continuous area of positive change in the
Pacific Ocean relative to the other basins (Fig. 3e-j), which is found in all warming scenarios. For
land and nearshore regions, the simulations also indicate an increase in TC stalling-induced daily
rainfall in the future (Fig. 3d). The positive change in the NA basin is the most dramatic in the
future, approaching 30% in the “Far Future” scenario. Moreover, although projections indicate that
the daily rainfall triggered by TC stalling is expected to be significantly less than that triggered by
non-stalling storms globally, the projected increase in daily rainfall triggered by TC stalling is
approximately 5.5-6.6% and 3.1-13.0% greater than that triggered by non-stalled tracks over land
and nearshore regions of the EP and NA basins, respectively (Supplementary Fig. 8a-f). Moreover,
TC stalling ratios over land and nearshore regions of these two basins are also projected to increase
in the future. These results suggest a greater flood risk to North America caused by stalled than
non-stalled tracks.

Examination of the dynamical mechanisms for TC stalling

To understand dynamical factors that induce the TC stalling, we constructed basin-specific
eXtreme Gradient Boosting (XGBoost) models26 incorporating both storm characteristics (i.e.,
longitude, latitude, maximum sustained wind speed (WindMax)) and storm-local environmental
variables (see Methods). These variables include Steering Wind, the angle difference between TC
motion and steering wind (Angle Diff), vertical wind shear (VWS), the area of high-altitude terrain
(High Alt. Area), and the presence of coexisting TC interactions. Our XGBoost models have good
out-of-sample prediction performances across all basins, with the Area Under Curve (AUC) values
exceeding 0.83 and classification accuracy above 75% (Supplementary Table 2). The model in the
NA basins achieved the highest performance, while that in the SI basin, though slightly lower,
remained highly reliable. These robust metrics indicate that our XGBoost models have good
prediction performance without overfitting, providing a reliable basis for interpreting dynamical
drivers via SHapley Additive exPlanations (SHAP)27. The SHAP value (see Methods) is an
interpretable artificial intelligence approach that represents the contribution from each feature to
each individual prediction. A positive SHAP value indicates a contribution toward an increased
stalling ratio and vice versa. The feature importance is defined by the mean value of absolute SHAP
value, reflecting the sensitivity of stalling ratio to the dynamical drivers.

Our SHAP analysis shows that the magnitude of the Steering Wind and the Angle Diff exhibit
high feature importance across multiple basins (Fig. 4 and Supplementary Fig. 9-14). The
probability of stalling scales positively with Angle Diff and negatively with steering wind speed.
Physically, a large directional difference implies a misalignment between the TC motion and the
steering flow, often inducing track deflection or deceleration1. Consequently, Angle Diff
consistently ranks among the top two predictors in most basins (excluding the NA basin).
Geographic coordinates (Longitude and Latitude) emerge as critical features, including
potential influences on TC from the Coriolis force, SST, and large-scale circulation regimes such
as the trade winds, westerlies, and subtropical highs. The background steering wind varies
significantly among different circulation regimes. For instance, in the SH, latitude exhibits a
monotonic influence on stalling (Supplementary Fig. 13-14). This is because the steering wind in
low-latitude regions is climatologically weak due to the South Pacific Convergence Zone and the
weak Coriolis force17,28, and TCs are constrained by the subtropical high in the SH29, making them
more likely to remain in low-latitude areas and thus increasing the stalling ratio. As for longitude,
it only shows an evident monotonic influence on stalling in the NA basin (Supplementary Fig. 12).
Specifically, the stalling ratio rises as TCs traverse westward toward the American continent. In
the eastern and central Atlantic (extending from the African coast), TCs are typically embedded
within the robust easterly trade winds along the southern periphery of the subtropical high, which
facilitate rapid westward propagation and suppress stalling. As storms reach the western NA and
the vicinity of the American continent, the steering environment often deteriorates. This region is
frequently characterized by the weakening of the subtropical high and the emergence of anomalous
blocking highs, resulting in weak or opposite environmental steering flows that are conducive to
stalling23.

With the exception of the SP basin, VWS generally has a negative relationship with stalling
(Fig. 4). Dynamically, weak vertical shear fosters vertical coupling of the vortex. This structural
coherence minimizes internal dynamical asymmetry, allowing the TC to be efficiently advected by
the ambient steering flow. Conversely, strong shear induces vertical tilting and decoupling. The
interaction between the displaced upper-level anticyclonic (clockwise in the Northern Hemisphere)
circulation and the lower-level cyclonic (counterclockwise in the Northern Hemisphere)
circulation can induce track deviations1,30,31, ranging from accelerated poleward drift to erratic
looping or stalling. In the WP and NA basins, extreme VWS values exhibit a complex duality,
being associated with both stalling and non-stalling events (Supplementary Fig. 10 and 12), likely
related to the vertical wind profiles32,33. Notably, the SP basin presents a distinct anomaly,
displaying an inverse relationship. Meanwhile, the steering wind at most TC track points in the SP
basin is relatively low (falling below the baseline; Supplementary Fig. 14a). This implies that
stalling in the SP basin is governed by a “quiescent environment” regime: stalling typically occurs
when the entire vertical wind profile is weak, characterized by low wind speeds across all levels
and small vertical differences in both speed and direction. Compared with the above-mentioned
storm-local environmental variables, WindMax, High Alt. Area, and the coexisting TC interactions
play subordinate roles across all basins.

Climate change impact on TC environments

Building on the observational insights, we further investigate the impact of climate change
on TC stalling by analyzing Steering Wind, Angle Diff and VWS. To quantify the influence of
these storm-local environmental variables under changing climate conditions, we solve the logit
equation to obtain their critical threshold that corresponds to the probability of TC stalling in each
basin in the “Historical” scenario. For example, the critical Steering Wind threshold in the WP
basin is 3.5 m/s, which yields the mean probability of stalling during the simulated historical period
(23.7%). The critical thresholds of storm-local environmental variables in each basin are shown in
Supplementary Table 3. The response to VWS is non-monotonic: since both excessively high and
low values are generally unfavorable for stalling, VWS is characterized by dual thresholds (VWS
plots in Fig. 4 and Supplementary Fig. 9-14). We defined the specific ranges delimited by these
thresholds as “stalling-inducing intervals.” The TC stalling ratio is higher when the values of
environmental variables are in these stalling-inducing intervals (Supplementary Fig. 15).

We calculated the ratio of TC track points where their storm-local environmental variable
values fall within the stalling-inducing intervals relative to the total track points (hereafter referred
to as exposure ratio), focusing on the scenarios and basins exhibiting statistically significant
changes in the TC stalling ratio. This exposure ratio reflects the probability of TC exposure to
stalling-inducing environments. Table 1 shows that, in basins and scenarios with significant
changes (indicated in bold), the signal of change in the stalling ratio aligns with that in the Steering
Wind exposure ratio. This suggests that alterations in the probability of TC exposure to the stalling-
inducing large-scale steering flow are the primary modulator of stalling variability in a changing
climate. In the WP basin, although the Angle Diff exposure ratio exhibits a positive change
(conducive to stalling) across the “No Warming,” “Near Future,” and “Far Future” scenarios, the
significant and more pronounced reduction in Steering Wind exposure ratio and VWS exposure
ratio dominates the signal. On the other hand, the climatological differences in Steering Wind
between scenarios also suggest that the northward winds intensify across most parts of this basin
reducing TCs’ time in low-latitude areas under warming climate (Supplementary Fig. 16). The
mean TC translation speed is higher in high-latitude areas than in the low-latitude ones16. For the
NA and SI basins in the “No Warming” scenario, all three ratios of environmental variables showed
consistent changes with the stalling ratios. Conversely, the SP basin under the “Far Future”
scenario presents a more complex situation involving competing signals. Although both Angle Diff
exposure ratio and VWS exposure ratio show positive changes, their impact is limited due to the
insignificant change of the Angle Diff exposure ratio and the low feature importance of VWS in
this basin (as determined by SHAP; Supplementary Fig. 14b). Consequently, the reduction in
stalling ratio in this basin is likely driven by the significantly reduced probability of TC exposure
to weak steering winds.

The probability of exposure to stalling-conducive environments is also linked to the spatial
distribution of TCs. In the SH, the analysis of the spatial changes of track points (Supplementary
Fig. 17-18) and the spatial distribution of environmental variables (Fig. 5a-c) shows that the
increased stalling ratio in the SI basin (“Historical” relative to “No Warming” scenarios) is
associated with a higher frequency of TCs occurring in low-latitude regions (0-20°S). Conversely,
the SP basin exhibits a decline in the stalling ratio under the “Far Future” scenario, which may be
due to a reduced density of TC tracks in the low latitudes.

Methods
Data

The observed TC tracks are obtained from version 4 of International Best Track Archive for
Climate Stewardship (IBTrACS)55,56, which merges recent and historical TC data from multiple
agencies to create a unified, publicly available, best-track dataset. We use records from the
following agencies: National Hurricane Center (NHC), Joint Typhoon Warning Center (JTWC),
and Central Pacific Hurricane Center (CPHC). To compare observed TC tracks with simulated TC
tracks, we delete records with missing locations, those not at 00:00, 06:00, 12:00 or 18:00 UTC.
The observed TC rainfall is obtained from the Multi-Source Weighted-Ensemble Precipitation
(MSWEP) V2.857. This dataset merges rain gauge, satellite, and reanalysis data to obtain a rainfall
estimate dataset with a 0.1° latitude × 0.1° longitude spatial resolution and a 3-hour temporal
resolution. We use the gauge-corrected historical version of MSWEP data, available from 1979.
To investigate the possible dynamical mechanisms of TC stalling, observed environmental
variables were derived from the European Centre for Medium-Range Weather Forecasts (ECMWF)
Reanalysis v5 (ERA5) with a spectral resolution of T639 (nominally 31 km)58. The main variables
used in this study include 6-hourly horizontal wind s at the 850 hPa and 250 hPa isobaric levels.
Although 200 hPa is conventionally used to represent upper-level flows in TC analysis59, we
selected the 250 hPa level to ensure vertical consistency and comparability with the output levels
of the high-resolution ensemble climate simulations used in this study. Topographic features were
characterized using the Global Multi-Resolution Terrain Elevation Data 2010 (GMTED2010) at a
spatial resolution of 30 arc-second60.

The period of study for this research is 1982-2019. Compared to the pre-satellite period, it is
considerably less likely that any TC would be undetected in the post-geostationary satellite era22.
Seven global TC basins covered in this study are defined in Fig. 1i.

Model and numerical experiments

Large-ensemble simulations were conducted with AM2.5C360, an atmospheric general
circulation model by the Geophysical Fluid Dynamics Laboratory (GFDL)61. AM2.5C360 has a
horizontal resolution of about 25 km and is able to simulate many aspects of the observed TC
variability over the past few decades during which reliable observations are available62. The
AM2.5-C360 model generates realistic TC annual cycles and response to El Niño-Southern
Oscillation61,63. We track the TCs in the climate model outputs using an algorithm that identifies
features in the climate model output that satisfy several criteria resembling a real-world TC. The
algorithm searches six-hourly output of the surface pressure and wind speed, vorticity at 850 hPa,
and mid-level temperature fields to identify warm core cyclones61,64.

In the simulations for the historical scenario, we used the Atmospheric Model
Intercomparison Project (AMIP)-type ensemble simulations (ten ensemble members). The
“Historical” scenario representing the current climate is forced by SST from the Hadley Centre
Sea Ice and Sea Surface Temperature dataset version 165 (HadISST1) after groupwise bucket SST
bias correction43. All the ensemble members are forced by the same historical SST and differ only
in initial conditions. The “No Warming” scenario representing the absence of climate warming
effects is simulated with five ensemble members. In this scenario, the atmospheric model is the
same as the one of the historical scenario except that the radiative forcings are fixed at the year
1880 level and the specified SSTs are modified so that the 15-year lowpass of global mean monthly
SST anomalies (relative to the 1871-1890 monthly climatology) are subtracted at each ocean pixel.
Simulations representing the future climate under the Representative Concentration Pathway 4.5
scenario are divided into two periods: the “Near Future” scenario from 2022-2059, and the “Far
Future” scenario from 2062-2099. The future climate scenario is simulated with six ensemble
members. In this scenario, the atmospheric model is forced by radiative forcings from the RCP4.5
scenario66. The SSTs are from the RCP4.5 scenario experiment using the GFDL-CM2.5-FLOR
coupled climate model and bias-corrected by comparing monthly climatology with the HadISST1
observational data over the period of 2001-2020. Half of the six ensemble members of future
climate AM2.5C360 are forced by 10-ensemble mean SST from the coupled model to focus on the
global warming signal while the other half of the ensemble members are forced by the first
ensemble only SST to include some internal variability from the atmosphere-ocean coupling.

The difference between the “Historical” and “No Warming” scenarios represents the response
of TCs to climate change since the pre-industrial levels, while the difference between the “Near/Far
Future” and “Historical” scenarios represents the response of TCs to future climate change. Note
that, to clearly illustrate the changes brought about by rising temperatures, the scenario differences
in this study are all presented as warm scenarios minus relatively cold scenarios. The multi-year
average SST gradually increases in the “Historical,” “Near Future,” and “Far Future” scenarios.

TC stalling

TC stalling is defined as the behavior of a TC remaining within a 200-km radius for more
than 48 hours 3,4. The 200-km radius yields a large enough sample size for statistical estimation
and is close to the radius of the TCs’ inner core. Heavy rainfall and strong winds usually occur in
the inner core of the TCs and their surrounding areas67. The track points satisfying the above
criteria are termed “stalled points.” A stalled TC includes at least one stalled segment, defined as
a sequence of nine or more consecutive 6-hourly stalled points. If the stalled segments identified
based on different track points have at least one overlapping stalled point or if there is no break
between the different stalled segments, these stalled segments are combined into a single stalled
segment.

TCs with all the track points showing an MSW speed lower than or equal to 34 knots (17.5
m s−1) were excluded. To avoid the influence of extra-tropical transition on TC rainfall, the study
region is constrained between 30°S and 30°N. The TCs in the South Atlantic are removed because
the sample sizes are too small. The angular deviation between two successive 6-hourly track
vectors v1 and v2 is =cos−1(v1 \cdot v2 / |v1| |v2|). The TC translation speed (km h-1) of each track point
is calculated by dividing the distance (km) from that point to the next point by their time interval.
The absolute frequency is calculated by counting the number of 6-hourly TC track points.

The two crucial parameters in the definition of TC stalling are the minimum number of track
points of the stalled segment, reflecting “stalled time,” and the “minimum enclosing circle radius”
containing the corresponding stalled points. We calculated the cumulative density function of the
minimum enclosing circle radius with 24-hr, 36-hr, 48-hr, 60-hr, and 72-hr stalled time across all
the basins over 1982-2019 based on observations (Supplementary Fig. 20-24). The results indicate
that the number of identified stalled track points increases with increasing stalled time; conversely,
the number of stalled track points decreases with increasing minimum enclosing circle radius.
We selected 48 hours as the stalled time and 200 km as the minimum enclosing circle radius because
this combination is a compromise consideration between sample size and the extremeness of the
events, and it is similar to what was used in a previous study3.

TC rainfall

We aggregate the 3-hourly (MSWEP) rainfall fields into a 6-hourly field before assigning it
to each 6-hourly TC track point. Rainfall greater than 0.6 mm 6hr-1 at a grid point is associated
with the TC if the grid point is within 500 km from the TC center. Grids with rainfall greater than
0.6 mm 6hr-1 are defined as rainfall grids. Although significant rainfall may be observed outside
the 500 km range from the TC center, this study used the commonly accepted 500-km threshold as
a conservative choice to ensure greater confidence that the analyzed rainfall is caused by TCs. We
define the area affected by the TC as a circle with a radius of 500 km centered on the points of the
TC tracks.

We introduce several metrics to analyze the difference in climatology between stalled and
non-stalled TC track points. The “overlap ratio” is calculated as the intersection area of TC-induced
precipitation at two consecutive track points divided by their union area. For stalled segments, the
rolling 24h rainfall accumulation is determined by summing TC rainfall fields over each 24-hour
period and dividing the sum by the number of grid cells in the rainfall fields (with union areas
counted only once), following the same method for non-stalled segments. The total area of the
corresponding rainfall field is the rolling 24h rainfall area. Also, we calculated the rainfall intensity
and rainfall area for each 6-hourly track point.

We use daily rainfall to analyze the effect of climate warming on rainfall induced by stalled
and non-stalled tracks. Daily rainfall at a grid point is associated with the TC if the grid point is
within 500 km from the TC center at any time step of the day13. Grids with daily rainfall greater
than 1mm day-1 are defined as rainfall grids.

Defining storm-local environments and storm characteristics

The storm-local environments reflect the environment experienced by TC track points. The
6-hourly data from ERA5 reanalysis was used to analyze the observed TC stalling, while the daily
data from the AM2.5C360 model was linearly interpolated to a 6-hourly scale and then used to
analyze the simulated TC stalling. Environmental variables include Steering Wind, Angle Diff,
VWS, High Alt. Area, and the presence of coexisting TC interactions. The steering winds are
defined as the weighted average between winds at 850 hPa and 250 hPa59:

u_steering = 0.8×u_850 + 0.2×u_200 (1)

v_steering = 0.8×v_850 + 0.2×v_200 (2)

Vertical wind shear is computed as the square-root of the sum of the squared differences in
the zonal and meridional winds between 850 and 250 hPa. The Angle Diff quantifies the directional
misalignment between the TC motion and the environmental flow. It is defined as the absolute
angular difference between the TC motion vector and the steering wind vector, constrained within
a range of 0° to 180°. The TC motion vector is derived from the displacement vector extending
from the current track point to the subsequent position. High Alt. Area is defined as the total area
with an elevation exceeding 1000 m within a 500 km radius of the TC center. The 1000 m threshold
was selected based on idealized numerical experiments indicating that terrain with a maximum
height of this magnitude is sufficient to modify low-level flow of TC68. Coexisting TC interactions,
widely known as the Fujiwhara effect39, describe the mutual dynamical influence between two
spatially proximate cyclones driven by their respective vorticity fields, mass, and relative
positioning. In this study, a coexisting TC interaction is defined to occur when the distance between
the centers of two simultaneous TCs is no more than 2000 km.

Steering Wind (both zonal and meridional winds) and VWS were spectrally filtered to T11
resolution to remove cyclonic circulation features and retain only the large-scale, background
environmental fields. Ref. 14 found that 95% of the T639-resolution cyclonic circulation is
removed at a T11 truncation. Along-track sampling of mean values from the spectrally filtered
fields and topographic data within a 500 km storm-centered radius was performed to obtain the
storm-local environment variables.

Longitude and latitude serve as spatial proxies for the Coriolis force, SST, and large-scale
circulation regimes. Although some studies found that local warmer SSTs stimulate earlier
poleward TC recurvature in some cases69,70, detecting these thermal anomalies within a storm-
local analysis framework can be challenging, as they may be statistically obscured by the large-
sample. However, the dynamical changes induced by SST is effectively captured by Angle Diff.
Additionally, we used WindMax instead of the minimum pressure to characterize the intensity of
TC because WindMax is more complete in the IBTrACS.

Machine learning model and SHapley Additive exPlanations (SHAP) value

To investigate the dynamical drivers of TC stalling, we used the XGBoost algorithm26.
XGBoost is a scalable implementation of the gradient tree boosting framework71 that has
demonstrated good performance in various meteorological applications, including TC rainfall72
attribution and historical hurricane wind records73 reconstruction. Unlike traditional generalized
linear models, XGBoost utilizes an ensemble of decision trees to model high-order interactions
and non-monotonic relationships without requiring explicit functional form assumptions26.

The input feature vector consists of three storm characteristics (longitude, latitude, and
WindMax) and five storm-local environmental variables (Steering Wind, Angle Diff, VWS, High
Alt. Area, and coexisting TC interactions). The target variable is a binary label indicating whether
a TC track point stalls. We optimized hyperparameters using a 5-fold cross-validation strategy. To
prevent model overfitting, we divide the dataset into training (70%) and test (30%) sets, and
preserve the original ratio of stalling samples in both subsets. The test set was only used for final
model evaluation and never involved in the cross-validation-based hyperparameter tuning process,
thus avoiding data leakage.

We used the SHAP value method to interpret our XGBoost models. SHAP belongs to the
method of ex post interpretation, drawing inspiration from Shapley values in cooperative game
theory27. It provides a systematic mechanism to assign significance values to individual model
features, helping quantify the impact of each feature on model predictions by considering all
potential feature combinations74. The SHAP value for a specific feature represents its marginal
contribution to the prediction, averaged over all possible feature permutations. For a given
observation, the sum of SHAP values for all features, plus the model's base value, equals the actual
prediction output. The random seed initialization may significantly influence model training
outcomes. To ensure the robustness of our results, we did not rely on a single fixed random seed
for the data split; instead, all XGBoost and SHAP results reported in this study represent the
ensemble mean derived from 50 independent training iterations with varying random seeds. In
addition, the climatic characteristics of TC and its stalling definition determine the differences in
the spatio-temporal distribution and class distribution of the samples, and such differences may
affect the predictive ability of the model. Even so, the model still has good performances, with the
Area Under Curve (AUC) values exceeding 0.83 and classification accuracy above 75%
(Supplementary Table 2).

We utilized the TreeExplainer, a high-speed exact algorithm developed specifically for tree
ensemble models75. In our analysis, a positive SHAP value indicates that the feature drives the
model prediction towards “stalling,” while a negative value indicates a contribution towards “non-
stalling.” We defined the feature importance as the mean value of absolute SHAP value.
Furthermore, we analyzed SHAP dependence plots to reveal the functional form of the relationship
between environmental variables and the probability of stalling.

The training of the XGBoost model and the SHAP analysis were conducted on a device
equipped with an Intel Core i9-14900KF and a NVIDIA GeForce RTX 4070 GPU (GPU memory:
12GB) with data parallelization, with a total training time of 12 hours.

Validation of climate model simulations

Our high-resolution ensemble simulations reproduce important aspects of the observed
frequency and rainfall of TC stalling, providing confidence in exploring the impacts of climate
warming on TC stalling. Fig. 2e-h shows the spatial pattern of observed and simulated ratio and
daily rainfall of TC stalling based on the climate model results. The simulations well reproduce
the regions with high observed TC stalling ratios based on observations. Notably, even in the
region with a low total TC frequency, the simulations capture a local high stalling ratios in the
central North Pacific Ocean. The simulations reproduce the spatial pattern of daily rainfall well,
especially in capturing high values in the low-latitude central Pacific and northwestern Gulf of
Mexico (stalled region of Hurricane Harvey).

We calculate the time series of both observed and simulated TC stalling ratios and daily
rainfall under current climate conditions and estimate their long-term trends (Supplementary Fig.
4-7). In the NI basin, to avoid artifacts within the Indian Ocean gap, the validation period for TC
stalling ratio of the climate model is limited to 1999-2019, while the validation period for other
basins is 1982-2019. Additionally, in low- and mid-latitude regions without rain gauges, MSWEP
data rely primarily on reanalysis data until 1999, after which satellite data become the dominant
contributor57. Therefore, the validation period for daily rainfall from the climate model in all the
basins is limited to 2000-2019, given the homogeneity and reliability of the MSWEP data. The
results indicate that the simulations well reproduce the observed trends in TC stalling ratios and
daily rainfall over time. Although the climate model does not reproduce daily rainfall as accurately
as the frequency, the trends of ensemble mean and most members are within the confidence
intervals of the observed trends, indicating reliable model performance. We further evaluated the
simulation performance of the model under TC stalling definitions with multiple combinations of
the enclosing circle radius and the stalled time. The results indicate that the model consistently
reproduces observed TC stalling patterns, regardless of the specific definition applied
(Supplementary Fig. 25 - 34). This consistency confirms that our model’s performance is
physically grounded rather than being an artifact under specific definitions, demonstrating its
reliable capability to capture real TC dynamics.

However, some discrepancies between the simulations and the observations remain. Spatially,
the model underestimates TC stalling ratios in the low-latitude regions of the SH. In areas where
the observed stalling ratio is zero, the ensemble simulations produce low, but non-zero, values.
These differences primarily arise from internal variability and the statistical properties of ensemble
simulations2,21,76,77. The steering winds in these low-latitude regions are weak and volatile, and
small variations in initial sea surface temperature conditions can shift the position of narrow
convergence zones such as the SPCZ. Such sensitivity can lead to slight geographical
misalignments and, consequently, an underestimation of stalling ratios in the ensemble mean.

The occurrence of “simulated non‑zero stalling where observations record zero” reflects a
known advantage of ensemble modeling78: observational records represent only a single realization
of the historical climate, whereas multi-member ensembles sample a broader probability
distribution and can capture rare but physically plausible events that may not appear in the limited
observational sample.

From a temporal perspective, differences in long-term trends primarily reflect internal climate
variability. The observational record is relatively short and thus sensitive to interannual and
decadal oscillations16,22,79, while the ensemble mean filters out such noise and emphasizes external
forced, long-term signals. As a result, discrepancies between observed and simulated trends are
expected and generally fall within the confidence intervals of the observed estimates.

In addition, because the WP basin contributed a large fraction of global TC stalling events,
the model’s overestimation of daily rainfall in this basin influences the basin-wide and hemispheric
estimates more strongly than in other regions, where such biases are comparatively small.

Overall, despite these discrepancies, the simulations reproduce the main spatio-temporal
characteristics of TC stalling frequency and daily rainfall. To ensure the robustness of our
conclusions, we adopt a conservative analytical approach that focuses on climatological
differences across multi-decadal periods and emphasizes only those results that pass significance
tests across the ensemble.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "This manuscript investigates and analyzes the special behavior of tropical cyclone (TC) stalling at the global scale, presenting the climatology and trends under climate change. The topic is interesting, and the overall flow of the manuscript is clear. However, there are several key issues that prevent me from providing more positive feedback, particularly concerning the reliability of global circulation models (GCMs) in analyzing TC stalling. My detailed comments are provided below, which I hope will be useful to the authors.\n\nMajor Comments\n1. Reliability of GCMs for TC stalling analysis\nMy main concern lies in the reliability of using GCMs to analyze TC stalling. Even with a high-resolution model such as AM2.5C360 (25 km) from Princeton, the resolution is still insufficient to fully capture TC structures. Moreover, most stalling events are characterized by abrupt changes in TC trajectories, which GCMs are generally poor at simulating. I strongly recommend that the authors carefully address this issue by presenting sufficient cases from both observations and simulations to justify the validity of using GCMs for stalling studies.\n2. Definition of TC stalling\nThe definition of TC stalling should be clearly and explicitly stated in the manuscript. Although Hall & Kossin (2019) and Zhang et al. (2024) adopted a similar definition, their studies were conducted mainly in the Northern Hemisphere. The authors here report more frequent stalling in the Southern Hemisphere, likely due to slower translation speeds (Kossin et al., 2019). If the definition is based on the relative proportion of slow-moving TCs, then the threshold may need to be adjusted for different basins. Before proceeding with the analysis, the authors should first discuss the robustness and applicability of their definition across regions.\n3. Unclear changes in the Western Pacific (Figure 4)\nIn Figure 4, the changes in the Western Pacific are difficult to interpret as contiguous or robust. Instead, the signal appears scattered and statistically weak, with many isolated spots on the map. The authors should carefully check the statistical significance of these results to ensure their robustness.\n4. Novelty relative to previous work\nAlthough this manuscript claims to be a global study of TC stalling, much of the analysis appears similar to Zhang et al. (2024). For instance, Figure 1 largely reproduces results already reported in that study. Rather than repeating known results, the authors should emphasize the differences among basins, highlight potential mechanisms underlying stalling in different regions, and provide a more systematic analysis of the environmental changes that influence stalling globally. This comparative and mechanistic focus would strengthen the novelty of the work.\n\nMinor Comments\nLine 74: I suggest adopting a threshold based on distance (e.g., kilometers), rather than degrees, as in Hall & Kossin (2019), to avoid the influence of varying latitudes on the degree measure.\nLine 82: The use of “although” is not appropriate in this sentence; please reconsider the logic.\nLines 85–86: The number of stalled TC points is less meaningful; the ratio of stalled cases to total TCs is more informative.\nLines 215–216: The causes of stalling should be discussed at this point, rather than left implicit.\nKossin, J.P. A global slowdown of tropical-cyclone translation speed. Nature 558, 104–107 (2018).\nhttps://doi.org/10.1038/s41586-018-0158-3\nHall, T.M., Kossin, J.P. Hurricane stalling along the North American coast and implications for rainfall. npj Clim Atmos Sci 2, 17 (2019). https://doi.org/10.1038/s41612-019-0074-8\nZhang, L., Cheng, T. F., Lu, M., Xiong, R., & Gan, J. (2023). Tropical cyclone stalling shifts northward and brings increasing flood risks to East Asian Coast. Geophysical Research Letters, 50(10), e2022GL102509.", "reviewer_2": "This manuscript focuses on global distribution of stalling storms and the associated rainfall under climate change scenarios. This is a timely and important piece. While some literature exists, this analysis provides an insightful geographic perspective looking at multiple basins and the variation in behavior. The data and methods are extremely well described, and I was left without unanswered questions. They are written in a reproducible way, which readers will greatly appreciate. For verification of availability, I used the Zenodo site and was able to see the available information. In fact, the information has already been downloaded 23 times as of the writing of this review. I think this manuscript is very close to being ready for publication – with only a few line items mentioned below. Thus, I give minor revisions for this manuscript.\n\nLine Items\nLine 76: add “global” to the sentence before “…global stalled and nonstalled” – so that it is clear that this figure includes all basins together in the depiction of the variables.\nLine 88: It is difficult to discern any difference in Figure 1e related to rainfall intensity – are you using Figure 1f to support this statement? It would be helpful to guide the reader to exactly the panel you wish them to consider when describing your results.\nLine 91: could this be due to generally colder ocean temperatures in the eastern Pacific leading to less rainfall?\nLine 151: In Trepanier et al 2024, we found near-coastal stalls are increasing in the NA. Include if you think it is relevant. We didn’t specifically state it is global warming related, but the data support an increase.\nLine 288: Supplementary Figure 8 does not define the basins. I think you mean Figure 1i within the actual manuscript.", "reviewer_3": "Review of: “Global stalled tropical cyclone in a changing climate” by Deng et al.\nRecommendation: Reject\nThe authors generate a tropical cyclone stalling global climatology and its projected changes under a warming climate. The study combines observations (IBTrACS tracks and MSWEP reanalysis data) with ~25 km GCM simulations (AM2.5C360) in AMIP and historical/future ensemble configuration using a unique scenario (RCP4.5). This work is subdivided into two future periods (Near Future 2022-2059 and Far Future 2062-2099). The main conclusion is that while the global probability of tropical cyclone stalling decreases, the daily rainfall associated with these TCs increases significantly. This precipitation increases are particularly over land and coastal regions, with increases approaching ~30% in North America at the end of the century.\nOverall, the manuscript is generally well-written and provides helpful insight in the topic. However, it bears some important flaws which make it of poor quality for this journal: lack of a clear methodology, statistically significant results, the analysis done to reach the findings, and the findings themselves. Detailed comments are listed to help improve the paper, which requires a very high and substantial major revision before it can be considered for publication in a high-quality journal like Nature Communications.\n\nGeneral comments:\n1. The main findings in this work are not novel enough to be published in this very high-quality journal, given the number of studies concluding an increase in precipitation in TCs. Along these lines, I do not understand the title, as the main findings and analysis are not related to the stalling itself but to the precipitation. On the other hand, the quality of this work would increase notably if it focused on analyzing TC stalling itself, why it stalls and how it could be reduced in the future (i.e., the main dynamical drivers for this reduction), instead of only studying precipitation related to these TCs.\n2. Definition of tropical cyclone stalling, rainfall attribution to TC and their robustness. I have some concerns about the tropical cyclone stalling definition because 200-km could be an arbitrary threshold. It could be interesting to do a sensitivity analysis changing the radius (from 150 to 250, for example) and the minimum duration (from 36 to 60 h). In this line, could be interesting testing other radius in the rainfall attribution due to your 500-km fixed threshold may include environmental/monsoonal rainfall. I suggest to test with other radius (400 to 600 km) and rainfall thresholds (0.3 to 1 mm/6 h; 0.5-2 mm/day).\n3. Although the authors used ten ensemble members of the same model, I have some concerns about using a unique future scenario (RCP4.5) and only one model. Including another scenario and several more models would help to capture a wider range of possible responses and improve confidence in the findings. For instance, the use of HighResMIP models could be advised. Or studying this phenomenon in a coupled climate model as we are dealing with TCs.\n4. Dynamical changes. The manuscript needs an insightful discussion of the main dynamical factors that potentially drive the changes in the TC stalling over each basin. The author includes in the manuscript a dynamical attribution which are discussed from other studies. However, this work needs a dynamical analysis to find what are the changes in their simulations. By including this, the quality of this work would increase notably.", "reviewer_4": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature Communications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career Researchers who co-review manuscripts."}, "author_response": "Response: Thank you very much for your comments on our research and manuscript. We have addressed all your comments point-by-point below and revised the manuscript accordingly.\n\nMajor Comments\n1. Reliability of GCMs for TC stalling analysis\nResponse: Thank you for your constructive comment. While internal TC variability (such as structural asymmetry) influences TC motion, the occurrence of TC stalling is largely governed by the external large-scale environment. GCMs are capable of effectively capturing the TC response to these external environmental forcings (e.g., large-scale circulation and coexisting TCs). Previous studies have demonstrated that GCMs and reanalysis datasets can reasonably simulate tropical cyclone tracks, with performance improving significantly as spatial resolution increases (from ~200 km to ~25 km)1–3. The spatial resolution of the AM2.5C360 model used in this study is finer than that of most existing models in CMIP6 HighResMIP and is comparable to ERA5. Consequently, the model is well-equipped to capture the characteristics of TC tracks.\nThe two crucial parameters in the definition of TC stalling are the minimum number of track points of the stalled segment, reflecting “stalled time,” and the “minimum enclosing circle radius” containing the corresponding stalled points. To better enhance readers' confidence in the AM2.5C360's simulated TC stalling performance, we evaluated the model's performance under different definitions with multiple combinations of the enclosing circle radius and the stalled time. The sensitivity test included the definition of stalling from previous studies4–6. The results indicate that the model consistently reproduces observed TC stalling patterns, regardless of the specific definition applied (Supplementary Fig. 25-34 of the revised manuscript). This consistency confirms that our model's performance is physically grounded rather than being an artifact under specific definitions, demonstrating a confirmation of the validity of using GCMs for TC stalling studies.\nWe have added relevant descriptions in the Validation of climate model simulations section of the Methods section in the revised manuscript.\n\n2. Definition of TC stalling\nResponse: TC stalling is defined as the behavior of a TC remaining within a 200-km radius for more than 48 hours. The two crucial parameters in the definition of TC stalling are the minimum number of track points of the stalled segment, reflecting “stalled time,” and the “minimum enclosing circle radius” containing the corresponding stalled points. Refer to the analysis of the definition of stalling in Zhang et al.4, We calculated the cumulative density function of the minimum enclosing circle radius with 24-hr, 36-hr, 48-hr, 60-hr, and 72-hr stalled time across all the basins during the 1982-2019 period based on observations (Supplementary Fig. 20-24 of the revised manuscript). The results indicate that the number of identified stalled track points increases with increasing stalled time; conversely, the number of stalled track points decreases with increasing minimum enclosing circle radius. We selected 48 hours as the stalled time and 200 km as the minimum enclosing circle radius because this combination is a compromise between sample size and the extremeness of the events.\nMoreover, similar definition is used in a previous study4, which facilitates readers' comparative analysis and discussion. We have added relevant descriptions in the TC stalling section of the Methods section in the revised manuscript.\nWe did not use a relative proportion (percentile-based threshold) definition because a fixed threshold can more obviously show the frequency differences in TC stalling characteristics among basins. In this study, our primary metric is the “stalling ratio,” which reflects the probability of a TC undergoing stalling. Using a percentile-based threshold for each basin would complicate the physical interpretation of the stalling ratio and obscure inter-basin variations. In addition, fixed thresholds have also been adopted in defining other extreme TC behaviors, such as Rapid Intensification (RI), which is usually defined as an increase in maximum sustained winds of at least 30 knots (~55 km/h) within 24 hours7–10. Even though the frequency of RI varies significantly across basins, this fixed definition remains the widely accepted standard. We apply a similar logic to the definition of stalling to maintain physical consistency.\n\n3. Unclear changes in the Western Pacific (Figure 4)\nResponse: To address this comment, we have supplemented the results of the significance test in all maps reflecting differences between scenarios (Fig. 3, Fig. 5, and Supplementary Fig. 18 of the revised submission).\n\n4. Novelty relative to previous work\nResponse: We acknowledge that we adopted the visualization style of Figure 1 from Zhang et al. (2024), as this format offers high information density and comprehensively illustrates the characteristics and cumulative hazard effects of TC stalling. However, our study presents numerous novel findings that have not been revealed in previous works. For instance, the Eastern Pacific (EP) basin is unique in that the accumulated rainfall from TC stalling is smaller than that from non-stalled storms, which is closely related to sea surface temperatures. Additionally, we found that basins in the Southern Hemisphere are more prone to stalling than those in the Northern Hemisphere. We have added more analyses of TC stalling results from different basins in the Results section. To better highlight the novelty of our work, we have included supplementary explanations and brief discussions regarding specific phenomena in certain basins (Lines 92-96 and Lines 102-106), thereby emphasizing the differences among basins.\nMoreveor, as suggested, we have dedicated a significant portion of the manuscript to further exploring the potential dynamical mechanisms of stalling. We constructed basin-specific XGBoost models incorporating both storm characteristics and storm-local environmental variables. We then used the SHapley Additive exPlanations (SHAP) method to interpret these models, analyzing the impact of key dynamical factors on the occurrence of stalling in each basin. We also investigated the primary dynamical drivers of climate warming impact on stalling by calculating changes in stalling-inducing environments. These additional results and analyses further clarify the novelty of our results with respect to the existing literature.\nPlease refer to the Results section and Discussion section of the revised manuscript for details.\n\nMinor Comments\nLine 74: I suggest adopting a threshold based on distance (e.g., kilometers), rather than degrees, as in Hall & Kossin (2019), to avoid the influence of varying latitudes on the degree measure.\nResponse: Thank you for your suggestion. We have changed this distance-based threshold from 2 degrees to 200km, and the related calculation results have also undergone slight changes (Lines 81-84, Fig. 3 and Supplementary Fig. 8 of the revised submission), but the main results and conclusions of this study remain largely unchanged.\nLine 82: The use of “although” is not appropriate in this sentence; please reconsider the logic.\nResponse: We have revised this sentence to make it more fluent (Lines 89-92 of the revised manuscript).\nLines 85–86: The number of stalled TC points is less meaningful; the ratio of stalled cases to total TCs is more informative.\nResponse: Thank you for your suggestion. We have supplemented the statement regarding the TC stall ratio in the Results section and added relevant information in Fig. 1, Supplementary Fig. 1-3, and Supplementary Table 1 of the revised submission.\nLines 215–216: The causes of stalling should be discussed at this point, rather than left implicit.\nResponse: We have significantly expanded the analyses of the dynamic mechanism of TC stalling in the Results section, but we still put the discussion on the interaction of coexisting TCs in the discussion section.\n\nReviewer #1 (Remarks on code availability):\nThe code is comprehensive, but the explanation is limited.\nResponse: Thank you for appreciating the quality of our effort in reproducing the analyses, consistent with the positive assessment by Reviewer 2 as well.\n\nReviewer #2:\nResponse: Thank you for your positive assessment. To further enhance the quality of our research, we have supplemented the analysis of the dynamic mechanisms of TC stalling. Overall, we have addressed all the comments point-by-point below.\nLine Items\nLine 76: add “global” to the sentence before “…global stalled and nonstalled” – so that it is clear that this figure includes all basins together in the depiction of the variables.\nResponse: We have revised this sentence to make it clearer (Line 84).\nLine 88: It is difficult to discern any difference in Figure 1e related to rainfall intensity – are you using Figure 1f to support this statement? It would be helpful to guide the reader to exactly the panel you wish them to consider when describing your results.\nResponse: Thank you for your suggestion. We have revised this sentence to guide the reader to the precise panel (Line 99).\nLine 91: could this be due to generally colder ocean temperatures in the eastern Pacific leading to less rainfall?\nResponse: Due to the small TC stalling rainfall area and its small overlap area within the EP basin, the local rainfall accumulation induced by TC stalling is also limited. This is an important reason why the accumulated rainfall from TC stalling is smaller than that from non-stalled storms in the EP basin. Notably, TC rainfall area is controlled primarily by its surrounding sea surface temperature (SST) relative to the tropical mean SST (that is, the relative SST)11. Since the SST in the EP basin is lower than that in other regions, this lower SST indeed explains why the rainfall patterns of stalling and non-stalling TCs in the EP basin differ from those in other basins. Your suggestion provides valuable insights for further explaining the differences among basins. We have revised this sentence to make it easier to understand (Lines 102-106).\nLine 151: In Trepanier et al 2024, we found near-coastal stalls are increasing in the NA. Include if you think it is relevant. We didn’t specifically state it is global warming related, but the data support an increase.\nResponse: Thank you for your suggestion. Our results show that past climate warming has led to a reduction in TC stalling in the land and nearshore areas of the NA basin, which seems different from the results by the Reviewer. We believe that this difference is primarily attributable to differences in the spatial extent of the study areas: our analysis encompasses a broader domain covering all the land and nearshore regions in the NA basin (including the land and offshore areas of regions such as Cuba, the Dominican Republic and Puerto Rico), while their investigation focuses on the coastline of the American continent within the NA basin (see Fig. 2 in Trepanier et al.5). To avoid confusion among readers and reconcile the two studies, we have included a brief comparison and discussion of these results in Lines 169-174 of the revised manuscript.\nLine 288: Supplementary Figure 8 does not define the basins. I think you mean Figure 1i within the actual manuscript.\nResponse: Thank you for your suggestion. We have revised this sentence to make it clearer (Line 456 of the revised manuscript).\n\nReviewer #2 (Remarks on code availability):\nIt is reproducible and usable. I also comment on this in my main review.\nResponse: Thank you for the positive assessment.\n\nReviewer #3:\nResponse: Thank you for your comments and suggestions. we have addressed all the comments point-by-point below and revised the manuscript accordingly.\nGeneral comments:\n1. The main findings in this work are not novel enough to be published in this very high-quality journal, given the number of studies concluding an increase in precipitation in TCs. Along these lines, I do not understand the title, as the main findings and analysis are not related to the stalling itself but to the precipitation. On the other hand, the quality of this work would increase notably if it focused on analyzing TC stalling itself, why it stalls and how it could be reduced in the future (i.e., the main dynamical drivers for this reduction), instead of only studying precipitation related to these TCs.\nResponse: While previous studies showed that total TC-related rainfall is projected to increase under warming scenarios, this general projection does not clarify how the rainfall contributions from stalling versus non-stalling TCs might diverge in the future.\nOur results reveal that, in the future, precipitation associated with stalled TCs over North America is projected to increase more substantially than that from non-stalled TCs: highlighting the amplified rainfall potential of stalled TCs in a warming climate has not been found in previous research. This is also a notable and interesting regional difference.\nMost previous studies have only focused on the impact of climate warming on TC rainfall, without paying attention to the response of rainfall brought about by the specific movement behavior of TC under climate warming. For instance, Hurricane Ike, a hurricane with rapid movement, and the slow-moving Hurricane Harvey incurred roughly similar economic losses. That said, the factors behind these losses differed: Harvey’s damage was driven by heavy rainfall, which caused extensive flooding, whereas Ike’s losses stemmed from storm surge-induced flooding and strong wind gusts. The meaning and contribution of the rainfall part of our study lie in clarifying how the rainfall from hurricanes like Harvey and that from hurricanes like Ike are expected to change under a warming climate.\nAs suggested, we have dedicated a significant portion of the revised manuscript to further exploring the potential dynamical mechanisms of stalling. We constructed basin-specific XGBoost models incorporating both storm characteristics and storm-local environmental variables. We then used the SHapley Additive exPlanations (SHAP) method to interpret these models, analyzing the impact of key dynamical factors on the occurrence of stalling in each basin. We also investigated the primary dynamical drivers of climate warming impact on stalling by calculating changes in stalling-inducing environments. These additional results and analyses further clarify the novelty of our results with respect to the existing literature.\nPlease refer to the Results section and Discussion section of the revised manuscript for more details.\n\nDefinition of tropical cyclone stalling, rainfall attribution to TC and their robustness. I have some concerns about the tropical cyclone stalling definition because 200-km could be an arbitrary threshold. It could be interesting to do a sensitivity analysis changing the radius (from 150 to 250, for example) and the minimum duration (from 36 to 60 h). In this line, could be interesting testing other radius in the rainfall attribution due to your 500-km fixed threshold may include environmental/monsoonal rainfall. I suggest to test with other radius (400 to 600 km) and rainfall thresholds (0.3 to 1 mm/6 h; 0.5-2 mm/day).\nResponse: The two crucial parameters in the definition of TC stalling are the minimum number of track points of the stalled segment, reflecting “stalled time,” and the “minimum enclosing circle radius” containing the corresponding stalled points (refer to analysis of the definition of stalling in Zhang et al.4). We calculated the cumulative density function of the minimum enclosing circle radius with 24-hr, 36-hr, 48-hr, 60-hr, and 72-hr stalled time across all the basins over the 1982-2019 period based on observations (Supplementary Fig. 20-24 of the revised manuscript). The results indicate that the number of identified stalled track points increases with increasing stalled time; conversely, the number of stalled track points decreases with increasing minimum enclosing circle radius. We selected 48 hours as the stalled time and 200 km as the minimum enclosing circle radius because this combination is a compromise consideration between sample size and the extremeness of the events. Moreover, similar definition is used in a previous study4, which facilitates readers' comparative analysis and discussion. We have supplemented the relevant descriptions in the TC stalling section of the Methods section.\nRegarding the rainfall attribution, we acknowledge that using a fixed 500-km threshold may inadvertently include some environmental or monsoonal rainfall. However, this methodology is widely adopted in the majority of TC rainfall studies. While it is a relatively conservative approach, it is computationally efficient. Although sensitivity analyses for precipitation are interesting, they can significantly increase computational costs, especially in light of the other two parameters the Reviewer already asked us to consider. Our analysis of rainfall is mainly to demonstrate the cumulative effect of heavy rain brought by TC stalling, which is an important part of why stalling is highly destructive (Hurricane Harvey is a typical example). Our research focuses more on stalling itself and its dynamical drivers. The relationship between rainfall and stalling is very interesting, and it is worth conducting a separate research on rainfall induced by stalling in the future.\n\n2. Although the authors used ten ensemble members of the same model, I have some concerns about using a unique future scenario (RCP4.5) and only one model. Including another scenario and several more models would help to capture a wider range of possible responses and improve confidence in the findings. For instance, the use of HighResMIP models could be advised. Or studying this phenomenon in a coupled climate model as we are dealing with TCs.\nResponse: First, we selected the RCP4.5 scenario as it serves as a reasonable \"middle-of-the-road\" baseline, reflecting likely outcomes assuming the continuation of current climate policies. Given the substantial computational resources required for these high-resolution simulations, we prioritized the RCP4.5 scenario to provide a representative projection of future conditions within our available capacity.\nSecond, the use of several members from a single model allows us to examine the role of internal variability, Furthermore, the performance of the AM2.5C360 model has been extensively validated in previous peer-reviewed studies. AM2.5C360 is able to simulate many aspects of the observed TC variability over the past few decades during which reliable observations are available12–14. The AM2.5-C360 model generates realistic TC annual cycles and response to El Niño-Southern Oscillation and tropical cloud13,15.\nThird, the spatial resolution of AM2.5C360 is finer than that of the majority of climate models currently available in CMIP6 HighResMIP, enabling it to capture TC track characteristics with high fidelity. Compared to standard CMIP6 models, AM2.5C360 also provides a more comprehensive set of variables necessary for our detailed mechanism and precipitation analyses. To better enhance readers' confidence in the AM2.5C360's simulated TC stalling performance, we evaluated the model's performance under different definitions with multiple combinations of the enclosing circle radius and the stalled time. The sensitivity test included the definition of stalling from previous studies4–6. The results indicate that the model consistently reproduces observed TC stalling patterns, regardless of the specific definition applied (Supplementary Fig. 25-34 of the revised manuscript). This consistency confirms that our model's performance is physically grounded rather than being an artifact under specific definitions, confirming the reliability of using AM2.5C360 model for TC stalling studies.\nWe have added relevant descriptions in the Validation of climate model simulations section and Model and numerical experiments section of the Methods section in the revised manuscript.\nFinally, we agree that TC stalling is a complex phenomenon with many aspects remaining to be explored. While investigating the variability of TC stalling across a diverse multi-model ensemble and a wider range of emission scenarios would be valuable, such an extensive comparison is substantial enough to warrant a separate, dedicated study. Future research would also benefit from using even finer spatial resolutions, such as coupled nested regional climate models or convection-permitting models, to further resolve the impacts of internal TC structure on stalling behavior.\n\n4. Dynamical changes. The manuscript needs an insightful discussion of the main dynamical factors that potentially drive the changes in the TC stalling over each basin. The author includes in the manuscript a dynamical attribution which are discussed from other studies. However, this work needs a dynamical analysis to find what are the changes in their simulations. By including this, the quality of this work would increase notably.\nResponse: As suggested, we have dedicated a significant portion of the manuscript to further exploring the potential dynamical mechanisms of stalling. We constructed basin-specific XGBoost models incorporating both storm characteristics and storm-local environmental variables. We then used the SHapley Additive exPlanations (SHAP) method to interpret these models, analyzing the impact of key dynamical factors on the occurrence of stalling in each basin. We also investigated the primary dynamical drivers of climate warming impact on stalling by calculating changes in stalling-inducing environments. These additional results and analyses further clarify the novelty of our results with respect to the existing literature.\nPlease refer to the Results section and Discussion section of the revised manuscript for details.\n\nReviewer #4 (Remarks to the Author):\nI co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature Communications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career Researchers who co-review manuscripts.\nResponse: We appreciate the transparency regarding the co-review process. We are very supportive of the Nature Communications initiative to facilitate training and provide appropriate recognition for Early Career Researchers. We would like to extend our gratitude to both the primary Reviewer and the Co-Reviewer for their time and the comprehensive feedback provided in the reports, which have been instrumental in improving the quality of our manuscript."}, {"round": 2, "reviewer_comments": {"reviewer_1": "Review of ‘Global stalled tropical cyclone in a changing climate’ by Deng et al.\nI appreciate the authors’ substantial effort in responding carefully to the reviewers’ comments and their constructive attitude throughout the revision. However, I remain concerned—consistent with my previous review and broadly aligned with Reviewer 3—that the manuscript may still fall short of the novelty and robustness typically expected for a high-impact journal such as Nature Communications. My main concerns are as follows:\n\n1. Model–observation discrepancy and implications for projections.\nBased on the newly added Figures S25–S34, the global stalling trends appear to differ substantially between the MMM and the observational estimates. This raises a critical concern about the reliability of the model ensemble for inferring future changes in TC stalling. The manuscript would benefit from a clearer discussion of why these discrepancies arise and how they affect confidence in the projected future trends.\n2. Limited conceptual novelty relative to prior work.\nIn my view, the study primarily extends previous regional stalling analyses to a global scale. While this is a useful synthesis, it is not yet clear that the manuscript offers fundamentally new insights into TC behavior or a distinctly innovative study design beyond geographic expansion. Stronger articulation of the unique scientific contribution (e.g., precipitation features of TC stalling before landfall) would be necessary to justify publication in Nature Communications.\n3. Mechanistic understanding remains insufficient despite added ML analyses.\nI acknowledge the authors’ additional analyses using XGBoost and SHAP to assess the relative importance of candidate drivers. However, the key limitation in TC stalling research is often the lack of a physically grounded explanation and process-level mechanism, rather than a purely statistical attribution of dominant factors. As currently presented, the machine-learning-based results do not yet translate into a clear physical narrative or testable mechanistic framework (e.g., dynamical pathways linking environmental changes to stalling behavior).\n\nGiven these concerns, I suggest the authors consider submission to a strong, more specialized venue such as npj Climate and Atmospheric Science or Communications Earth & Environment, where the manuscript may be better aligned with the journal’s scope and expectations. In my opinion, the work is likely to meet the standards of those journals.", "reviewer_3": "I have reviewed the revised manuscript and am pleased to report that the authors have successfully addressed all of my previous concerns. It is evident that a significant amount of hard work has gone into improving the quality and clarity of the paper following my review. The scientific content is now robust, and the arguments are well-supported. I am happy to recommend this manuscript for publication. However, before final acceptance, there are several minor spelling, grammar, and syntax errors that need to be corrected.\n\nPlease see the list of specific technical corrections below:\nPage 2, Line 39: The text currently reads: \"...one of the costliest No Warming disasters in U.S. history.\"\nCorrection: This should be corrected to \"natural disasters\".\nPage 2, Line 44: The text reads: \"...potentially associated with low-frequency No Warming variability.\"\nCorrection: This should be corrected to \"natural variability\".\nPage 3, Line 71: \"Furthermore, changes in probability of TC stalling in climate warming is mainly affected by...\"\nCorrection: It should read: \"...changes... are mainly affected by...\"\nPage 14, Line 388: \"Our simulations show that the ratio of TC stalling are declining...\"\nCorrection: It should read: \"...the ratio of TC stalling is declining...\"\nPage 3, Line 118: \"...there is artifact in the observations...\"\nCorrection: This should read: \"...there is an artifact in the observations...\"\nPage 13, Figure 5 Caption (Line 337) & Figure 3 Caption: \"...based on the absolute frequency of TC stalling at least the 25th percentile...\"\nCorrection: This phrasing is slightly awkward. It should likely read: \"...at least at the 25th percentile...\" or \"...exceeding the 25th percentile...\"\nPage 14, Line 400: \"...the role of this factor on the stalling changes caused by warming is not as much as that of the strength...\"\nCorrection: I suggest changing this to: \"...is less significant than that of the strength...\"\nPage 15, Line 413: \"...while others that global TC translation speed would increase...\"\nCorrection: It should read: \"...while others suggest/project that global TC translation speed...\"\nPage 18 Line 525: \"...it is similar to what used in a previous study...\"\nCorrection: This should read: \"...similar to what was used in a previous study...\"\nPage 16, Line 439: \"...as well as those not in 00:00, 06:00...\"\nCorrection: It should read: \"...those not at 00:00, 06:00...\"\nPage 17, Line 488: \"...half ensemble members are forced...\"\nCorrection: This should read: \"...half of the ensemble members...\"\nOnce these minor issues are resolved, the manuscript will be in excellent shape. Congratulations to the authors on a very interesting study.", "reviewer_4": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature Communications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career Researchers who co-review manuscripts."}, "author_response": "Response: Thank you very much for your comments on our manuscript. We have addressed all your comments point-by-point below and revised the manuscript accordingly.\n\n1. Model–observation discrepancy and implications for projections.\nResponse: Thank you for your constructive comment. Based on a comprehensive evaluation of both spatial patterns and temporal trends, we find that the model demonstrates a reliable capability to reproduce TC stalling characteristics within the expected margins of uncertainty for climate simulations.\nFrom a spatial perspective, the high-resolution ensemble successfully reproduces the observed hotspots of TC stalling and the spatial patterns of stalling-ratio changes across multiple definitions of stalling (i.e., combinations of enclosing-circle radius and stalled time). While some regions in the simulations exhibit locally high stalling ratios (e.g., parts of the southeastern Pacific), these areas contain very few TCs, and thus have negligible influence on basin-scale statistics.\nFrom a temporal perspective, the simulated and observed trends are statistically consistent, even when they appear visually different in isolated cases. Both trends are estimated using ordinary least squares, and the simulated and observed trend lines lie close to each other across basins and definitions. Although in a few instances the trend signs differ, the trend magnitudes for the multi-model mean (MMM) and for most ensemble members lie within the confidence intervals of the observational trends, indicating no statistically significant discrepancy. These differences reflect acceptable levels of uncertainty inherent in climate modeling rather than a fundamental deficiency in model performance.\nWe acknowledge several known limitations. First, the model underestimates stalling ratios in low-latitude regions of the Southern Hemisphere. In areas where observations show zero stalling, the simulations may produce low, but nonzero, values. These differences arise from internal model variability and ensemble sampling properties. In regions with weak steering flows such as the SPCZ, small differences in initial conditions can shift the position of narrow convergence zones, affecting low-latitude stalling estimates. Furthermore, instances in which “observations are zero but simulations are non-zero” reflect a well-understood advantage of ensemble simulations: observations represent only a single realization of historical climate, whereas ensembles sample a broader distribution that includes rare but physically plausible events not captured in the historical record.\nDifferences in temporal trends are largely attributable to internal climate variability. The observational record is relatively short and sensitive to interannual to decadal oscillations, while the MMM suppresses such noise and primarily reflects externally forced, long-term signals. Thus, some degree of mismatch between observed and simulated trends is expected, and, as noted above, these differences fall within observational confidence intervals. Additionally, year-to-year matching between observed and simulated TC outcomes is not feasible given current model resolution and simplified atmosphere–ocean coupling. Accordingly, our evaluation focuses on climatological behavior, consistent with standard practices in previous studies.\nWe recognize the inherent uncertainties of climate models. To ensure robustness, we adopted a conservative analytical framework that emphasizes long-term climatological differences and retains only results that pass statistical significance tests across multiple ensemble members. This approach ensures that our projected future changes reflect robust signals rather than noise or model artifacts.\nIn summary, we acknowledge that discrepancies exist between observations and simulations. However, our analysis demonstrates that:\n1. the spatial patterns of TC stalling are well reproduced;\n2. simulated and observed trends are statistically consistent across all basins;\n3. remaining discrepancies fall within established expectations of internal variability and ensemble sampling; and\n4. the revised Methods section (“Validation of climate model simulations,” Lines 677–704) now explicitly clarifies these points.\nTherefore, the model–observation discrepancies do not undermine confidence in the projected future trends, and we have strengthened the manuscript accordingly.\n\n2. Limited conceptual novelty relative to prior work.\nResponse: Thank you for your comment. We respectfully clarify that this study offers several contributions that go beyond a geographic extension of previous regional analyses. Specifically, the manuscript advances the scientific understanding of TC stalling in three distinct and substantive ways, each addressing gaps not previously resolved in the literature.\nFirst, our global analysis reveals previously unrecognized spatial characteristics of TC stalling. Prior studies have focused mainly on the Western North Pacific and North Atlantic, implicitly suggesting that these basins dominate stalling behavior. In contrast, our results show that the highest stalling ratios occur in the Southern Hemisphere basins, a finding not obtainable from regional studies alone. This global perspective identifies new stalling hotspots and provides a unified climatological framework that allows regional results to be placed in a broader physical context.\nSecond, the study provides a more comprehensive and physically grounded understanding of the mechanisms driving TC stalling. Whereas earlier work has typically relied on case studies or basin-specific attribution analyses, we perform a global, quantitative assessment that connects large-scale circulation, storm-local environments, and internal vortex structure into a coherent mechanistic narrative. This includes clarifying the relative importance of steering wind magnitude, directional misalignment, vertical wind shear, and geographic controls across basins. Furthermore, our results show that the contribution of binary TC interactions is substantially more limited and more basin-dependent than previously inferred from regional studies, advancing the physical interpretation of stalling dynamics and preventing overgeneralization of mechanisms such as Fujiwhara interactions.\nThird, this study represents the first global evaluation of TC stalling in a warming climate using high-resolution global climate model simulations. While previous work has used pseudo-global warming frameworks or regional climate models to explore specific events or specific basins, a consistent global assessment of stalling response to climate change has been lacking. Our results demonstrate that changes in the probability of TC exposure to weak steering environments act as the primary modulator of stalling variability under warming. We also evaluate, for the first time, the ability of high-resolution GCMs to reproduce stalling behavior, establishing a new modeling benchmark for the field.\nTaken together, these three advances demonstrate that this work is not a simple geographic extension, but instead provides new insights into global stalling behavior, a new mechanistic framework, and new climate-change projections unavailable in prior literature.\nWe have revised the Introduction and Discussion to more clearly articulate these points and highlight the novelty of the study.\n\n3. Mechanistic understanding remains insufficient despite added ML analyses.\nResponse: Thank you for this thoughtful and constructive comment. We agree that a statistical ranking of predictors alone is insufficient and that a clear, physically grounded mechanistic explanation is essential for understanding TC stalling. In the revised manuscript, we have therefore substantially strengthened the mechanistic component of the study.\nWhile our original submission included physically motivated interpretations of the SHAP results, we have now gone further by synthesizing these findings into a coherent, process-based framework that explains TC stalling across three spatial scales:\n1. Large-scale synoptic environment, which establishes the background steering flow and its spatial climatology (e.g., the influence of subtropical highs, trade-wind regimes, and low-latitude weak-flow regions).\n2. Storm-local environment, which exerts direct kinematic forcing on the vortex. This includes the magnitude of the steering wind and the directional misalignment between the storm motion and environmental flow, two of the most influential variables identified by SHAP and physically linked to track deceleration and deflection.\n3. Internal vortex structure, which modulates the storm’s response to external forcing. In particular, strong vertical wind shear can induce vortex tilting and asymmetric upper- and lower-level circulations, resulting in irregular track deviations or stalling.\nThis three-scale organizational framework integrates the machine-learning results with established TC dynamics (e.g., baroclinic tilt, environmental steering theory, and shear-induced track modulation), providing the process-level explanation the reviewer requested. The revised Discussion (Lines 385–404) now presents this mechanistic narrative explicitly and links the ML-identified drivers to dynamical pathways that are consistent with physical theory and prior literature.\nAdditionally, we clarify the basin-specific role of binary TC interactions. While previous regional studies have emphasized this mechanism, our global analysis shows that its influence is limited and heterogeneous, and in many cases implicitly captured by changes in the local steering environment. This finding further refines the mechanistic interpretation of stalling at the global scale.\nTogether, these revisions transform the ML-based attribution into a testable, physically interpretable mechanistic framework, directly addressing the Reviewer’s concern and improving the clarity and scientific value of the manuscript.\n\nResponse to Reviewer 3:\nWe sincerely appreciate the Reviewer’s positive assessment of our revised manuscript and their recognition of the extensive efforts invested in strengthening the analysis, clarity, and overall scientific contribution. We are grateful for the Reviewer’s recommendation for publication. We have carefully addressed all of the minor spelling, grammar, and syntax corrections listed in the review. We have also thoroughly proofread the entire manuscript to ensure the flow, grammar, and presentation quality now meet the journal’s standards. We thank the Reviewer once again for the constructive and supportive feedback, which has helped us substantially improve the clarity and readability of the manuscript.\n\nResponse to Reviewer 4:\nWe appreciate the transparent explanation of the co‑review process. We fully support the Nature Communications initiative to provide training opportunities and formal recognition for Early Career Researchers involved in peer review. We would also like to express our sincere gratitude to both the primary Reviewer and the Co‑Reviewer for their time and for the thoughtful, detailed feedback provided in the reports. Their comments have been extremely helpful in improving the quality and clarity of our manuscript."}, {"round": 3, "reviewer_comments": {"reviewer_1": "I would like to express my sincere appreciation to the authors for their very careful and thoughtful consideration of my suggestions throughout the two previous rounds of review. I have also learned a great deal from this review process. At this stage, I am convinced that the topic of tropical cyclone stalling warrants a global study such as this one. Therefore, I recommend acceptance of this paper and extend my congratulations to the authors."}, "author_response": ""}]

Decision letter