Tropical-leaning Atlantic Oscillation favors more typhoons toward Asian high-latitude cities

Domain: earth_science
Published: 2025-12-31
Editorial outcome: major_revision

Abstract
Poleward migration of Northwest Pacific typhoons brings severe impacts on East Asian high-latitude cities, yet early typhoon climate prediction remains a long-standing scientific challenge. Here we reveal a seemingly-familiar-yet-strange climate oscillation phenomenon, which we name Tropical-leaning Atlantic Oscillation (TAO). Statistical results show that springtime TAO can explain 56% of the variance in a dominant dipole mode of typhoon track variations during July–September of 1979–2023, suggesting that it possesses a robust predictive skill of peak-season typhoon tracks four months in advance. Specifically, springtime TAO is characterized by a sea-level pressure seesaw between the tropical North Atlantic and the Hudson Bay-Davis Strait, relating to the meridional shift of North America-Atlantic subtropical jet stream. It generates cross-seasonal North Atlantic- and Pacific surface sea water temperature anomalies, thereby triggering Northwest Pacific cyclonic steering flows that tempt (obstruct) typhoons toward East Asian high-latitude (low-latitude) cities during July–September. Climate models project an increasing frequency of positive TAO events. This may potentially contribute to a poleward migration of typhoon activity toward East Asian high-latitude cities as climate warms, yet uncertainty remains due to model biases in simulating tropical surface sea water temperature patterns. Our results highlight an overlooked impact of an emerging internal climatic oscillation on the enhancing typhoon risks toward high-latitudes.

Introduction
Typhoons (also called hurricanes) rank among the most destructive natural disasters, often cause great economic losses and mortality. As the climate warms, typhoon activity is becoming stronger and more frequent at higher latitudes than in the past. The poleward migrations of typhoons pose escalating threats to coastal communities at higher latitudes, particularly under the anticipated prevalence of future climate scenarios. Therefore, assessing the climate change impacts on nearshore typhoon activity is becoming an important research arena.

Many studies have focused on the responses of typhoon activity to externally forced climate change, with particular focus on variations in surface sea water temperature (SST), subsurface ocean heat, sea ice, or warming Tibetan Plateau. Impacts of North Pacific and Atlantic climate shifts on typhoon activity are also highlighted. For example, interdecadal changes in Northwest Pacific (WNP) tropical cyclone (TC) activity can be attributed to the Atlantic Multidecadal Oscillation (AMO), Interdecadal Pacific Oscillation (IPO) and/or Pacific Decadal Oscillation (PDO). While the impacts of shifting internal atmospheric climatic oscillations on changes in typhoon activity are often overlooked. Projections of shifting climate oscillation impacts on poleward expansion of typhoon threats are therefore inherently uncertain. However, policy-makers expect the quantification of the potential impacts and predictive ability ahead of typhoon season to operationally plan management options.

Poleward migration of WNP TC exhibit prominent seasonality, which is related to the distinct responses of late-season and peak-season TC occurrence to a stronger Pacific Walker Circulation. Although the recent WNP typhoon migration trends during peak season (July–September) are well attributed to the warmer subtropical SST due to the increasing greenhouse-gas emissions and Pacific mega-El Niño-Southern Oscillation (ENSO) forcing, the WNP typhoon activity may be responding more strongly to the changes in the prominent internal natural climate oscillations, such as the North Pacific Oscillation (NPO), North Atlantic Oscillation (NAO), and Northern Annular Mode (NAM). Because this climate variability tends to influence the evolution of tropical and extratropical SST modes across seasons, which in turn modulate the later atmospheric circulations, and thereby indirectly affect typhoon activity.

Here we find that the poleward migration mode of East Asian typhoon activity during peak season is strongly connected to a seemingly-familiar-yet-strange climate oscillation in preceding spring (March–May), which shows a pressure seesaw between the low-latitude North Atlantic and the high-latitude Hudson Bay-Davis Strait. The southern part of this oscillation is located at the tropical North Atlantic, considerably distinct from the classical extratropical NAO, thus, we call it the Tropical-leaning Atlantic Oscillation (TAO). There are several points that need to be emphasized: First, due to the great fame and widespread climate impacts of the classical NAO, it stealthily shields other tropical–extra-tropical atmospheric oscillations. In addition, the TAO appears only during cold seasons when the NAO is most robust, but substantially weakens during warm seasons when the NAO is relatively weaker. As shown in Fig. 1a, once the NAO signal was removed, the cross-latitudinal correlation of sea level pressure (SLP) can directly capture the previously-overlooked TAO signal. These may be the reasons why the TAO was not identified earlier.

It is well known that the NAO mode often expands to the Mediterranean Sea in the Eastern Hemisphere. In contrast, the southern part of TAO can expand to deep tropics and the whole TAO pattern is located in the West Hemisphere, closer to the tropical Pacific. Just as this prominent spatial feature, TAO is more capable of inducing tropical SST climate anomalies, and thereby can significantly modulate typhoon activity in the following summer. The following results will reveal that the TAO mode can provide a perspective for an in-depth exploration of the poleward migration of East Asian typhoons, and also provide insights into the understanding of cross-ocean interactions.

Results
Identifying the spring TAO mode
After removing the extratropical NAO signal from the spring SLP anomalous fields, there are significant negative correlations between 0°–40°N and 50°N–70°N (Fig. 1a; NAO signals removed via linear regression) during 1979–2023. This suggests the existence of a meridional atmospheric oscillation in addition to the NAO over the North Atlantic in spring. The position of this oscillation (red circle in Fig. 1a) is clearly different from that of the NAO (blue circle in Fig. 1a and Supplementary Fig. 1a), and it is more oriented towards the tropical region (lower gray diagonal), which is why we refer to it as the TAO. We use region-averaged SLP to define this TAO mode (see “Methods” and red rectangles in Fig. 1b). The SLP pattern of TAO is also closer to the tropics compared to the extratropical NAO (Fig. 1b versus Supplementary Fig. 1b), especially the southern region south of 30°N. The TAO can be straightforwardly identified as the leading empirical orthogonal function (EOF) mode of spring SLP anomalies over the North America–Atlantic region (25°W–105°W, 10°–80°N), which explains a larger proportion of SLP variances (21.4%, calculated as R2 in the plotted regions, area-weighted; Fig. 1b and Supplementary Fig. 1b) compared with that of the extratropical NAO (16.9%) during spring over the North America-North Atlantic-Western Europe.

The spring TAO is closely linked to the North America-Atlantic subtropical jet shift. A positive phase of the TAO corresponds to a northward shift of the jet exit region (Fig. 1c). Moreover, the TAO index has a robust Pearson correlation coefficient up to 0.85 (p < 0.01) with the North America-Atlantic jet meridional shift index (NAJS; see “Methods”, Fig. 1c and Supplementary Fig. 3). Further analysis indicates that the TAO is mainly induced by the meridional shift of the North America-Atlantic jet stream. It is well known that divergence tends to occur to the north of the jet axis at the exit region of an upper-level jet, while convergence prevails to the south. As expected, a positive phase of the TAO corresponds to a northward shift of the upper-level jet in the exit region (Fig. 1c), which favors the convergence at 300–100 hPa between 10°–35°N, forcing air to descend into the lower troposphere and inducing high pressure in the subtropical surface (Fig. 1d). Meanwhile, divergence appears at 300–100 hPa between 35°–60°N, leading to low-level air being drawn upward and forming a low-pressure system at higher latitudes surface (Fig. 1d). This process generates a meridional pressure dipole characteristic of the TAO. The wave activity flux (WAF, see “Methods”) shows propagation to both meridional sides of the jet stream (Fig. 1e), with wave energy divergence in the mid-to-upper troposphere (Fig. 1f), indicating that the emergence of the TAO originates from jet variability. Additionally, upward-propagating WAF is evident in lower levels around 30°–40°N (Fig. 1f). This suggests that the ocean surface plays a role in TAO pattern, therefore, TAO is also partly associated with the local air-sea coupling. Whereas the NAO-related anomalies are mainly located in higher latitudes (Supplementary Fig. 1c, d) and NAO is influenced by local air-sea interactions and polar vortex (Supplementary Fig. 1e, f). Besides, a comparison with other known decadal modes (e.g., AMO and PDO) via the wavelet coherence spectral analysis shows that the TAO is indeed distinct from the NAO (Supplementary Fig. 4), further solidifying its uniqueness.

Spring TAO takes typhoon activities toward Asian high-latitude cities
Since the TAO is located closer to the tropics, we speculate that it may cause more significant tropical climate anomalies and thus be more significantly related to the WNP typhoons. Next, we consider the relationship between TAO and WNP typhoon activity. For the period 1979–2023, the TAO shows considerable potential in predicting the WNP typhoon activity toward East Asian high-latitude cities in peak season (Fig. 2a). As shown in Fig. 2b, the anomalous typhoon track dipole (TTD), identified as the leading EOF mode of Asia-Pacific typhoon track density (Supplementary Fig. 5a), can be effectively captured by preceding spring TAO. Of note is that the three extreme El Niño years (1982, 1997 and 2015) are excluded in this study, thus, here the TAO-TTD relationship is almost independent of ENSO to a large extent. Given that the relationship between TAO and ENSO is not significant (even in a longer period of 1958–2023; Supplementary Fig. 6a–e), we have tested and found that even in the case of excluding all ENSO years, the TAO–TTD relationship still keeps robust (R = 0.72, p < 0.01; Supplementary Fig. 6f, g). Accordingly, the cross-seasonal TAO-TTD linkages are generally independent of ENSO signals.

Besides, the TAO pattern can be also obtained by regressing the spring SLP anomalies onto the TTD index (Supplementary Fig. 5b). As such, the TAO index exhibits a strong correlation coefficient (R = 0.75, p < 0.01) with TTD index, substantially surpassing that of the extratropical NAO (R = 0.38; Fig. 2a). The spatial-correlation between the TAO-related typhoon track density and that of TTD reaches 0.94 (p < 0.01; Fig. 2b). Similar results can be confirmed with various TC best-track data (Supplementary Table 1 and Supplementary Fig. 7), suggesting that this cross-seasonal linkage from spring TAO to summer TTD is very robust and thereby can be used for seasonal forecasts of typhoon activities in East Asian cities. Of note is that NAJS exhibits a weaker correlation with TTD (R = 0.64), albeit NAJS and TAO are highly correlated (R = 0.85; Supplementary Fig. 3c). This may be due to that the NAJS index is defined from 200-hPa zonal wind anomalies within a smaller region of the upper-tropospheric jet stream. In contrast, TAO is a lower-tropospheric SLP mode that spans a broader tropical domain, the associated air-sea interactions are inevitably stronger than that for NAJS, leading to a closer linkage from TAO to TTD.

As shown in Fig. 2c, positive TAO can predict the above-normal typhoon occurrence frequency (measured by track density) and destructive powers (measured by Accumulated Cyclone Energy, ACE, see “Methods”) of typhoons 4 months in advance, for East Asian high-latitude cities such as Tokyo, Osaka, Kagoshima, Seoul, Qingdao, and Shanghai; meanwhile, the below-normal destructive powers of typhoons for East Asian low-latitude cities like Hong Kong, Manila and Haikou can also be foreseen. We further measure the typhoon frequency in each box in response to TAO anomaly (see bar in Fig. 2b). The typhoon frequency is calculated by counting typhoon in peak season entering the boxes in Fig. 2b. A one-standard-deviation change in the TAO index corresponds to an increase of 1.0±0.45 yr−1 in typhoon frequency in northern East Asia (see red box) and a decrease of 0.65±0.45 yr−1 in typhoon frequency in southern East Asia (see blue box). When normalized by box-area, the typhoon-frequency changes are similar in magnitude between the two regions, with about changes of 0.46 yr−1 and 0.41 yr−1 in typhoon frequency per million square kilometers in the southern and northern boxes, respectively.

Mechanisms of cross-season and cross-basin TAO impacts
Mechanisms of TAO performance in predicting TTD-like typhoon activity can be explained as follows. On the one hand, the springtime TAO can indirectly promote the subsequent Pacific Meridional Mode (PMM) SST anomalies through the TAO-related anticyclonic flows (Supplementary Fig. 8a, b). Since this anticyclonic flows can weaken local trade winds in the subtropical Northeast Pacific (red rectangle in Supplementary Figs. 9 and 11a), and thereby the resultant warm SST, mainly induced by wind–evaporation–SST feedback mechanism, facilitates PMM’s development via the seasonal footprint mechanism. On the other hand, the TAO can induce the North Atlantic Tripole (NAT) SST anomalies (Supplementary Fig. 8a). Specially, the TAO-related anomalous northeasterly winds over the tropical North Atlantic (TNA) can enhance the trade winds to cool SST there (blue rectangle in Supplementary Fig. 9). This cold SST can further strengthen the anticyclone over the eastern Pacific–North Atlantic via the Gill-type Rossby wave response. Therefore, this cold SST can persist into the following summer (Supplementary Fig. 8c) and further contribute to the development of PMM (Supplementary Fig. 8d) through a Gill-type Rossby wave response with an anticyclone to its west. And the physical process from TNA SST to PMM has been demonstrated by Ham et al. through observations and numerical experiments. However, the impact of TAO on TTD is much more pronounced than that of TNA SST (Supplementary Fig. 10), where only the northward-moving typhoon is significantly altered. This may be because that TAO not only affects Pacific climate through the persistent TNA SST anomalies in later seasons (Supplementary Fig. 10c, d), but also directly triggers the formation of the early prototype of PMM via the southern part anticyclonic circulation of TAO during spring (Supplementary Fig. 8a and Supplementary Fig. 11a).

Due to the TAO-promoted summer tropical SST changes, a significant atmospheric response can be induced over the WNP, especially the lower-level WNP cyclonic circulation anomaly (streamlines in Fig. 3a). Moreover, positive PMM linked to TAO can promote active convection over the subtropical North Pacific (Supplementary Fig. 11), inducing a Rossby wave cyclone over the WNP. The lower-level WNP cyclonic circulation can also be identified as the WNP branch of the Pacific–Atlantic subtropical oscillation (PASO), driven by the summer TNA SST anomalies via inducing subtropical Walker-cell-like changes (Supplementary Fig. 12) and Pacific convection response (Supplementary Fig. 11b, d), which further contribute to WNP cyclone. Correspondingly, there are coherent eastward shifts of enhanced WNP monsoon trough (MT), weakened tropical upper-tropospheric trough (TUTT) and strengthened South Asian high (Fig. 3a). Collectively, these processes favor the eastward shift of typhoon genesis (Fig. 3b) and reinforce the TTD mode of Asia-Pacific typhoon track (Figs. 3c and 2b). Here, we further confirm the relationship between TAO-related SST anomalies and the zonal shift of TUTT and MT. The NAT and PMM SST patterns can partially contribute to the longitude changes in TUTT and MT since their correlations are significant (Supplementary Fig. 13).

Since the strengthened and eastward South Asia high can induce strong descending motions to suppress typhoon genesis in the western part of the WNP. The eastward shift of the WNP MT and the TUTT can drive the favorable conditions for typhoon genesis eastward via changing the typhoon genesis conditions. These environmental modulations on typhoon genesis can be depicted by the Genesis Potential Index (GPI), which shows a zonal dipole (shading in Fig. 3b), suggesting more typhoon genesis over the central-eastern part of the WNP and fewer typhoon genesis over the southwestern part of the WNP (contour in Fig. 3b). To show the relative importance of thermodynamic and dynamic factors in the GPI analysis, a linear decomposition is applied (see “Methods”). The contributions of each variable are calculated, then the regional-mean is obtained for eastern and western regions (outlined in Fig. 3b) associated with positive and negative GPI anomalies, respectively (Supplementary Fig. 14). The increased GPI in the eastern WNP is mainly contributed by increase mid-level relative humidity, followed by increased lower-level vorticity, ascending motion and increased potential intensity (Fig. 3d). While the vertical wind shear does not significantly contribute to GPI changes. In contrast, the decreased GPI in the western WNP is dominantly contributed by increased vertical wind shear (Fig. 3e).

Furthermore, the typhoon track after genesis is shaped by the steering effects of atmospheric circulation (Fig. 3c). During positive TAO years, a strong anomalous cyclonic circulation dominates East Asia–Pacific during summer, characterized by easterly steering flows in the north and westerly steering flows in the south (vectors in Fig. 3c). High-latitude easterly steering flows are favorable for northwestward movement of typhoons, while low-latitude westerly steering flows prevent typhoon from traveling west. Consequently, more (fewer) typhoons move toward East Asian high-latitude (low-latitude) cities during peak season. And we also quantified the zonal steering flow changes (Fig. 3f), given that the meridional component of steering flow change is negligible. Both northern and southern regions (outlined in Fig. 3c) show significant changes, suggesting important guiding effects for typhoon movement. We further examined the above results after removing the linear trend and found that the relationship between TAO and TTD is still robust, and the related atmospheric and oceanic processes are also significant (Supplementary Figs. 15 and 16), indicating that the TAO mode and its climatic influence are not a signal of global warming trend, but internal climate variability.

TAO dominates the prediction for East Asian typhoon activities
Given that the spring TAO is a good simulator of peak season typhoon activity, we construct a simple model to predict the TTD time series (Fig. 4a) via multiple linear regression with some other potential prediction factors (see “Methods”). The regression equation derived from the training period of 1979–2014 is as follows:
Prediction_TTD = 0.682 × TAO + 0.268 × NAO − 0.257 × NAM + 0.086 × NPO − 0.061 × SIC − 0.019.

The Pearson correlation between observed and predicted TTD is 0.74 for the training period (1979–2014) and even reaches 0.89 for the validation period (2016–2023) (Fig. 4a), both far exceeding the 99% confidence level. The corresponding root mean square error (RMSE) is 0.63 for the training period, and only 0.54 for the validation period. The phase-sign-agreement is 83.3% for the training period and surprisingly reaches 100% for the validation period (Fig. 4a). This simple model exhibits excellent performance in predicting the TTD index, which can well predict the typhoon anomalies in East Asian coastal cities (Fig. 4b). It should be emphasized that the coefficient of the TAO in the regression formula is notably larger than those of other predictors, suggesting a dominant role of the TAO among the predictors. To further validate the stability of the TAO-TTD relationship and the prominent importance of TAO relative to other factors, we employed the bootstrap method to assess the error range of regression coefficients and used the sliding regression to test the temporal stability of coefficients (Supplementary Fig. 17). This prediction is generally independent of ENSO (Supplementary Fig. 18). Results indicate that only the TAO coefficient in the regression model is statistically significant. Although the relationship between TAO and TTD exhibits some fluctuations, it remains statistically significant throughout the study period.

We further compare the correlation pattern of Prediction_TTD (Fig. 4b) with other prediction models established only using TAO (Fig. 4c, d) and NAO (Fig. 4e and Supplementary Fig. 19). The Prediction_TTD explains 61.7% of the observed variances in TTD variations during 1979–2023 (evaluated by the R2 between indices; Fig. 4b), while the TAO alone explains a substantial 56.3% of the variation (Fig. 4c). When the NAO signal is removed, TAO maintains its strong linkage to the TTD pattern (Fig. 4d). In contrast, the extratropical NAO-model (Supplementary Fig. 19) reflects very low predictability (R2 = 14.7%). Moreover, when the TAO signal is removed, the predicted variances from the extratropical NAO decrease to approximately 0% (Fig. 4e), suggesting that the relationship between the extratropical NAO and TTD is not independent of the TAO. In other words, the majority of TTD predictability stems from the TAO. Hence, the TAO-model most closely reflects the Prediction_TTD (Fig. 4c versus b, d). Above results highlight the dominant role of TAO in shaping the poleward migration of typhoon activity, enabling us to prepare for typhoon disasters up to about 4 months in advance.

In addition to the above climate variables, SST variability often has a significant influence on typhoons. However, unlike metrics such as typhoon genesis location, frequency or intensity, the relationship between other SST variability and TTD remains unexplored. Therefore, we further calculate the correlation coefficients between the winter-spring SST indices and the TAO as well as the TTD. As shown in Supplementary Table 2, there was no significant linear correlation between TAO (or TTD) and other existing indices (Niño3.4, PDO, AMO, and Indian Ocean Dipole). This suggests that the influence of the TAO on the TTD is generally independent of these SST modes. These findings also highlight the unique predictive significance of the TAO for the northward shift of East Asian typhoons.

Projected increase of TAO events
Beyond its role in influencing the current typhoon activity, the TAO also offers insights into future typhoon climate changes. Using the Coupled Model Inter-comparison Projection Phase 6 (CMIP6) multi-member ensemble (CMIP6-MME) with historical and Shared Socioeconomic Pathway 5-8.5 (SSP585) scenario experiments, we further explore the future changes in TAO. As shown in Fig. 5a, the CMIP6 models generally (23 out of 26 models) project an increase (~39.4%) in positive TAO events during the 21st century (23.96±3.28 events per 100 years), compared to that during the 20th century (17.19±1.85 events per 100 years). Moreover, the TAO mode obtained from the CMIP6-MME well mirror the observed one (Figs. 5b versus 1b; with a robust pattern correlation, R = 0.92).

The increase in positive TAO event is most pronounced toward the end of the 21st century, exceeding 20 events per 100 years (Fig. 5c). Moreover, there are 7 out of 26 CMIP6 models can even capture the strengthening cross-seasonal linkage between the WNP anomalous cyclone and preceding spring TAO (Supplementary Fig. 20), further implying more prominent impacts on typhoon track potentially from TAO as the climate warms. In addition, the future anthropogenic warming would decelerate typhoon motions near high-latitude populated coastal regions due to a poleward shift of the subtropical westerlies, which can induce a longer time of typhoon impacts. And accompanying with increasing intensity and rainfall, this can potentially compound future typhoon-related damages. Based on the current results, the projected increase in positive events of TAO may potentially contribute to increases in steering-flow patterns conducive to poleward migration of typhoon tracks. Such circulation changes imply the possibility that high-latitude cities in East Asia would face more typhoon threats and relevant economic losses in the future. Therefore, here we boldly infer that the poleward migration of typhoon activity induced by TAO in a warming future would exacerbate the societal risk from typhoons in East Asian high-latitude cities as the century progresses.

It should be noted that our inference is based on the premise that the linkage between the TAO and TTD remains unchanged. Since the cross-seasonal linkage between the two keeps unchanged even after excluding the long-term trends (Supplementary Figs. 15 and 16). However, under the context of global warming, typhoon activity and tropical SST patterns are evolving, thus uncertainty is always an inevitable existence. For instance, the frequency of future typhoons may continue to decrease with global warming, which might alter the response of typhoon activity to TAO indirectly. Besides, CMIP6 outputs used here do not simulate typhoon tracks directly, and steering-flow changes alone cannot fully translate to future track density changes because the latter are also strongly affected by shifts in TC genesis location. Furthermore, considering that the influence of TAO on WNP circulations depends on teleconnection processes between the ocean and atmosphere, the response of WNP typhoon activity to the projected changes in TAO might be sensitive to the global warming-induced SST patterns in the tropics, and thereby leading to uncertainty in the cross-seasonal TAO-TTD linkage.

We similarly explored future changes in the frequency of positive phase NAO events (Supplementary Fig. 21). However, the multi-model results show a significant decrease in the frequency of positive NAO events. Considering the positive correlation between NAO and TTD (Fig. 2a), this contradicts the poleward shift of typhoon activity as climate warms. Therefore, the relationship between NAO and typhoon in the current observations may not be physically reliable, but rather mediated through TAO. However, the physical relationship between TAO and classical extratropical NAO requires more detailed investigations.

While we suggest that CMIP6 models show a robust projection of more frequent positive TAO events, some uncertainties remain. The spread among models primarily arises from differences in simulating the mean-state and variability of North Atlantic SSTs, as well as the subtropical jet response. It is reported that most CMIP6 models exhibit cold SST biases in the North Atlantic, especially in the tropics. This may cause an overestimation of positive TAO frequency because positive TAO corresponds to negative TNA SST anomaly. Nevertheless, the overall positive shift in TAO occurrence is consistent across most models, indicating that the projected intensification of positive TAO is a robust multi-model signal rather than an artifact of a few outliers. Future work could further assess the sensitivity of TAO projections to Atlantic SST biases and internal variability through targeted model experiments.

Methods
Typhoon data and processing
We focus on the East Asia–WNP typhoon (TC with Vmax > 32.6 ms−1) during July–September of 1979–2023, using the Joint Typhoon Warning Center TC best-track datasets. Although TAO exerts some influence on typhoon activity in other months, its relationship with the TTD mode is mainly concentrated in the peak season (i.e., July–September) when WNP typhoon are most active (Supplementary Fig. 22). Thus, we mainly focus on July–September. This study primarily focuses on typhoons because stronger typhoons are more likely to cause significant damage. We also examined results for all TCs with Vmax > 17.2 ms−1, which showed consistent findings (Supplementary Fig. 23), although the relationship with TAO may be somewhat weaker for weaker TCs due to their inherent randomness. The genesis (marked by Vmax > 17.1 ms−1) density and track density are calculated by interpolating typhoon passage onto 5° longitude × 5° latitude grid points. A 9-point spatial smoothing is applied to the typhoon track density in the plotted regions. The ACE data is the square of the maximum wind speed. The EOF method was applied to the normalized track density in (100°–145°E, 10°–45°N) to identify the TTD mode. We removed the signal of the original first EOF mode from all data via linear regression because it is mainly a climatic signal, with little anomaly to be predicted (Supplementary Figs. 24 and 25). We also use the TC best-track data from the Japan Meteorological Agency and the Shanghai Typhoon Institute of the China Meteorological Administration for validation.

Reanalysis data
We primarily use the monthly atmospheric reanalysis version 5 data from the European Center for Medium-range Weather Forecasts (ERA5, resolution of 2.5° × 2.5°). The SST data (resolution of 2° × 2°) are merged from the NOAA Extended Reconstructed SST version 5 (ERSSTv5) and the Hadley Centre Global SST version 1.0 (HadISST1). Three extreme El Niño years (1982, 1997, and 2015) are excluded, which may disturb the TAO-TTD relationship. However, we suggest that ENSO is likely to play a limited role in this relationship (Supplementary Fig. 6 and Supplementary Table 2). Including ENSO years will not significantly alter the results (Supplementary Table 1). The significance is evaluated by the Student’s t-test and linear regression.

Climate indices
This paper employs multiple climate indices, primarily atmospheric circulation mode, SIC and SST indices, with specific definitions provided in Supplementary Table 3. We define the spring (March–May) TAO and NAO indices based on the difference in SLP between two regions. We further compare our NAO index with the downloaded NAO index (see “Data availability”), which is the leading EOF mode of SLP in (90°W–40°E, 20°N–80°N), and find that the two time series show strong Pearson correlation (R = 0.97, p < 0.001) during spring. The SST index is defined as the regional average surface sea water temperature. We utilized multiple precursors from preceding winter and spring seasons (with specific seasons determined by high correlation coefficients in Supplementary Fig. 26) to highlight the unique and dominant role of TAO.

Genesis potential index
The GPI is used to diagnose the modulation effects of environmental conditions on the typhoon genesis:
GPI = |105η|1.5 × (RH/0.5)3 × (Vpot/70)3 × (1 + 0.1Vs)−2 × (1 + 10ω500)−1
where η is the 850-hPa absolute vorticity, RH the 700-hPa relative humidity, Vpot the maximum potential intensity, Vs the 850-hPa to 200-hPa vertical wind shear, and ω500 the 500-hPa vertical velocity.

Vpot is calculated as:
Vpot = [CpCKTs / CD T0]1/2 [lnθ*e − lnθe]1/2
Where Cp is the specific heat capacity at constant pressure, CK the air-sea heat circulation exchange coefficient, and CD the sea surface drag coefficient. Ts sea surface temperature, T0 the average temperature of the outflow layer, θ*e the sea surface saturation equivalent potential temperature, and θe the boundary layer equivalent potential temperature.

Vs is calculated as:
Vs = [(u200 − u850)2 + (v200 − v850)2]1/2
Where u is the zonal wind, v the meridional wind, and the subscript indicate the pressure level (hPa).

To attribute GPI anomalies associated with the TAO to individual environmental factors, a linear decomposition was applied as follows: for each environmental factor, the corresponding TAO-related anomaly was substituted into the GPI calculation, while all other variables were retained at their climatological states. The total GPI anomaly was subsequently approximated as the sum of the contributions from all factors. This approach enables the relative roles of vertical wind shear (VWS), low-level relative vorticity (Vor), mid-level vertical velocity (Omega), mid-level relative humidity (RH), and potential intensity (Vpot) in modulating TC genesis potential to be quantitatively assessed.

Wave activity flux
We use the wave activity flux (WAF) to effectively analyze the propagation characteristics of anomalously steady Rossby waves. The horizontal component of WAF is calculated as:
WAF = 1 / 2000|U| [U(ψ0x2 − ψ0ψ0xx) + V(ψ0xψ0y − ψ0ψ0xy)] i + [U(ψ0xψ0y − ψ0ψ0xy) + V(ψ0y2 − ψ0ψ0yy)] j
Where p is the pressure, ψ0 is the perturbation stream function relative to climatology, f is the Coriolis parameter, N is the static stability, and U and V are the climatology of zonal and meridional winds, respectively. |U| is the magnitude of (U, V). The subscripts x and y represent the zonal and meridional gradients, respectively.

Multiple linear regression model
Multiple linear regression model can be expressed as:
M_reconstruction = β0 + Σi=1n βi Ni
where β0 is the intercept, βi are regression coefficients, M_reconstruction is the reconstructed index, Ni is the index of variables used. The coefficients, in the form of matrix β, are estimated using the ordinary least squares method, minimizing the residual sum of squares:
β = (NTN)−1 NTM
β0 = M − Σi=1n βi Ni
where N is the design matrix including all variables, and M is the response vector. The upper horizontal line indicates the mean value.

CMIP6 data and processing
The CMIP6 historical and SSP585 simulations are used. We use 26 models (Supplementary Table 4) with historical experiments for 1850–2014 and SSP585 experiments for 2015–2100. For each model, only the first ensemble member available is selected. Most are r1i1p1f1, except for MIROC-ES2L and UKESM1-0-LL (r1i1p1f2). Data from different model resolutions are interpolated into the same horizontal resolution of 2.5° × 2.5°. We use a sliding climatology of 60 years to standardize the TAO index. For the first 60 years (1850–1909), climatology was fixed at 1850–1909. For years after 1910, the climatology was defined as the 60 years before that year. We use a threshold of +1.0 standard deviation to detect positive TAO events in CMIP6 model outputs. The definition of the TAO and NAO indices in models are the same as in observations.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "Comments on “A new climate oscillation takes typhoon activity toward Asian high-latitude cities” by Wu et al.\nThis study focuses an important issue of atmospheric mode on NWP typhoons. In general, this manuscript is well written and\nshows a clear story line of how TNAO impacts NWP typhoons. However, I really concern the novelty and robustness, which\ncould need extra analyses.\nMajor Comments:\n1. The authors show the TNAO impact on tropical cyclone over the NWP. However, I find that the pathway TNAO impact is\nmuch similar to the North Tropical Atlantic (NTA) SST impact on Pacific climate (Ham et al. 2013). In this sense, I considered\nthat the TNAO and NTA SST are air-sea coupled system that they share the same physical process affecting the NWP\nTyphoon. Moreover, the TNAO is relatively transient compared with the NTA and the statistical covariance is fragile.\nTherefore, the verification of TNAO impact on NWP typhoon activity in numerical models is of great importance.\n2. The authors considered the poleward migration of NWP typhoon under global warming. However, many studies have\nshown that the NWP typhoon frequency will decrease under the global warming (Knutson et al. 2020). The CMIP6 models\ncan only reflect the TNAO changes, however, the connections between NWP typhoons and TNAO may also change. The\nchanged NWP typhoons may also shape the NWP typhoon – TNAO relationship.\n3. The authors only considered the typhoons, which reach 32.6m/s and above, right? However, I believe that the TNAO\ncould affect both typhoons and tropical cyclones (17-32.6 m/s) in the NWP. The authors need to show the TNAO impact on\nall TC frequency and explain why they only focused on typhoons?\nMinor comments:\n1. Line 299: The definition of jet shift index could be incorrect. Please modify the definition region. Also, it is worthy noted\nthat the authors defined too many atmospheric modes in the Method part, I suggested to revise the definitions and explain\nthe purpose of the usage.\n2. It is unfair to move the TNAO signal from the NAO index since they are highly correlated in Fig.4 . The authors could also\ncheck the TNAO-TC track correlation when removing the NAO signal. I believe it is also weak and insignificant.\n3. Since the TNAO is induced by the jet shear, I also want to the correlations between North America jet stream and NWP\ntyphoons (and TNAO).\n4. Line 126: why do they authors only remove three strong El Niño events? I believe that the TNAO is highly correlated with\nthe NTA SST simultaneously and consequently ENSO signal. Therefore, it may also need to consider both El Niño and La\nNiña events.\n5. Line 273 and Line 73: the typhoon season is defined as the months from July to September or August? Note, the portion of\nJuly-September typhoons could not be a majority for the whole typhoon season tropical cyclone genesis frequency from\nJune to October.\nReferences:\nHam et al. Sea surface temperature in the north tropical Atlantic as a trigger for El Niño/Southern Oscillation events\nKnuston et al. Tropical Cyclones and Climate Change Assessment: Part II: Projected Response to Anthropogenic Warming", "reviewer_2": "Review of Manuscript \"A New Climate Oscillation Takes Typhoon Activity Toward Asian High-Latitude Cities\"\nThis manuscript presents a novel climate oscillation, the tropical-leaning North Atlantic Oscillation (TNAO), and explores its\npredictive capability for the poleward migration of typhoon activity in East Asian high-latitude cities. The study makes a\nsignificant contribution to the field by identifying a distinct oscillation from the classical North Atlantic Oscillation (NAO) and\ndemonstrating its robust predictive skill for typhoon tracks. The findings are particularly relevant for improving seasonal\nforecasts and disaster preparedness in the region under the background of global\nclimate change.\nThe identification of the TNAO as a distinct oscillation from the NAO is a major contribution. The results and conclusions in\nstudy are basically convincing. Also, the use of multiple datasets (e.g., ERA5, CMIP6) and statistical techniques (e.g., EOF\nanalysis, wave activity flux) strengthens the credibility of the findings. The exclusion of extreme El Niño years enhances the\nrobustness of the TNAO-TTD relationship. This study clearly links TNAO to jet stream variability, SST anomalies (PMM,\nNAT), and subsequent typhoon steering flows. The cross-basin teleconnection mechanism is well-articulated.\nMajor comments:\n(1)While the distinction from NAO is emphasized, further discussion is needed on why TNAO was not identified earlier. A\nspectral analysis or comparison with other known modes (e.g., Atlantic Multidecadal Oscillation) could solidify its\nuniqueness.\n(2)Please clarify whether TNAO is independent of ENSO on longer timescales, as only extreme El Niño years were\nexcluded.\n(3)the Gill-type Rossby wave response and \"subtropical Atlantic-Pacific teleconnection\" require more detailed explanation\nor references. A schematic diagram could aid understanding.\n(4)please address how TNAO-induced SST anomalies persist into summer. Is this due to ocean memory or atmospheric\nfeedback?\n(5)about the CMIP6 Projections: the 39.4% increase in positive TNAO events is intriguing but lacks discussion on model\nspread or uncertainties. How do model biases (e.g., in Atlantic SST) affect this projection?\nOther comments and suggestions:\n(1)the statement on decelerated typhoon motion seems tangential; either expand or omit.\n(2)The abstract overstates \"inevitably face more serious threats.\" Modify to reflect uncertainties in CMIP6 projections.\n(3)Discuss how TNAO’s predictive skill compares to existing indices (e.g., PDO, AMO).\n(4)Highlight limitations, such as the assumption of stationary TNAO-typhoon relationships under climate change.\nGenerally, this study makes a compelling case for the TNAO as a key driver of typhoon variability. With minor revisions to\nclarify mechanisms and contextualize findings, it would be a valuable contribution to high-impact journals. The reviewer\nrecommend acceptance after addressing the above points.", "reviewer_3": "Summary: The manuscript identifies and characterizes a novel springtime North Atlantic seesaw—termed the Tropical-\nleaning North Atlantic Oscillation (TNAO)—and demonstrates its predictive linkage to summer typhoon-track density (TTD)\nover the western North Pacific via modulation of the Pacific Meridional Mode (PMM). By defining a spring sea level pressure\n(SLP) difference index, the authors show robust correlations (R ≃ 0.75) with the leading empirical orthogonal functon(EOF)\nof typhoon tracks, far exceeding the classical NAO’s skill. They propose two cross-basin pathways: (1) TNAO-driven\nanticyclonic flow over the Northeastern Pacific weakens trades, warms SST, and seeds the PMM; and (2) an Atlantic SST\ntripole forces a Rossby response that reinforces the PMM. CMIP6 projections under SSP5-8.5 indicate increases in positive\nTNAO events, suggesting enhanced future typhoon poleward migration. Overall, the conceptual advance is substantial,\nlinking two ocean basins through a physically plausible teleconnection with seasonal\nforecasting potential.\nGeneral evaluation: This study presents a new cross-basin teleconnection with strong predictive implications, making a\nsubstantial and well‐validated contribution to our understanding of cross-basin climate–typhoon linkages and offering a\nseasonal forecasting potential. Pending clarifications on several issues described below and some extended analysis to\nstrengthen the robustness, I recommend acceptance after a major revision. I believe that once these points are satisfactorily\nresolved, the paper will be well suited for Nature Communications.\nMajor Comments\n1. Asian high-latitude cities: In the title, the authors emphasized the typhoon activity toward Asian high-latitude cities.\nHowever, throughout the manuscript, the reviewer could not find any analysis on landfalling typhoons that threatened cities\nin the relevant regions. The authors should explicitly demonstrate the point by showing the increase in landfall frequency. An\nanalysis of landfall frequency or distance from coast would help clarify the claim.\n2. Long-term trend vs internal climate variability: Given global warming, both Atlantic and Pacific climates have a warming\ntrend. Have the authors removed a linear (or higher‐order) trend from their SLP/SST fields before computing TNAO and\nPMM? Note that a lingering trend could spuriously enhance their teleconnection statistics.\n3. Memory from prior seasons: Spring TNAO events may be influenced by preceding winter’s modes (e.g., ENSO, IOD,\nPDO, AMO). Ensuring the predictor is not contaminated by autoregressive persistence will clarify the true lead‐time benefits.\n4. Pacific-basin wind-anomaly: The manuscript shows only the Atlantic (NAT/TNAO) 850 hPa anomaly map (Supplementary\nFig. 5a), but omits the analogous Pacific anomaly field demonstrating trade-wind weakening. Please include a spatial map\nof 850 hPa wind anomalies regressed onto the PMM (or TNAO) over the Pacific to visually confirm the westerly anomalies in\nthe 8–18° N band.\n5. Confounding climate modes: Other low-frequency modes (AMO, PDO/IPO), ENSO, and IOD may influence both TNAO\nand typhoon metrics, potentially biasing the TNAO→TTD link. The authors should perform multivariate or partial-correlation\ntests that control for AMO, PDO/IPO, ENSO (Niño 3.4), and IOD indices and to\nconfirm that TNAO retains unique predictive skill.\n6. Potential confounding from monsoon and shear: Summer monsoon trough position and vertical wind shear directly\nmodulate both genesis and track of typhoons over the western North Pacific. Even if PMM explains part of the signal, the\nresidual may simply be a shifted monsoon trough or the Pacific TUTTs. Could the authors provide any insights on possible\nconnection? At least, this should be discussed briefly to demonstrate the PMM’s unique pathway as discussed in the\nmanuscript.\n7. Regression-training sample size and robustness: Training the TNAO→TTD regression on 1979–2014 (36 yr) risks\nsampling uncertainty; the validation period (2016–2023) showing higher R² may reflect lucky sampling or non-stationarity.\nThe authors can conduct sensitivity tests (e.g., leave-one-decade-out, bootstrap/jackknife) to quantify confidence intervals\nand show windowed or running regressions over 1979–2023 to assess temporal stability of the linkage.\n8. Mechanistic quantification: The proposed pathways (thermodynamic PMM vs. direct circulation steering) are described\nqualitatively. Could the authors provide a quantitative analysis to show the relative importance of thermodynamic and\ndynamic factors in the GPI analysis and quantify the consistent steering change? The authors may also include\nprecipitation/OLR composites to verify convective amplification under the PMM.\n9. Operational predictability: To underscore novelty, showing how a simple TNAO‐based statistical forecast compares\nagainst state‐of‐the‐art dynamical seasonal typhoon outlooks (e.g., ECMWF, GFS) in hindcast mode would demonstrate\nadded value of the finding. Does the regression model outperforms the existing operational models?\n10. Physical process diagrams or schematic: Finally, a concise schematic that lays out both the direct anticyclone‐driven\ntrade weakening and the Atlantic‐tripole/Gill‐mode response pathway would help readers keep track of the multiple legs of\nthe cross-basin teleconnection.\n11. Translation from projected TNAO to northward shift of typhoon activity: The authors show an increased frequency of\npositive TNAO events under SSP5-8.5, but how that translates into societal risk (e.g. changes in ACE, landfall probability,\neconomic exposure) remains unexplored. A brief discussion would help readers grasp\nthe real-world implications. It will be much convincing if the authors can demonstrate\nthe connection by analyzing typhoon activity in CMIP6 models ensemble simulations.\nMinor Comments\n1. The global analyses (EOFs, regressions, composites) lack a clear statement of their full temporal coverage (e.g., 1979–\n2023) beyond the training/validation split. Therefore, please explicitly state the data period in the Methods and all figure\ncaptions (e.g., “analyses based on 1979–2023”).\n2. “high summer” vs. “peak season”: The manuscript alternates between “high summer” (July–August) and “peak season”\n(July–September), which can confuse readers. Please keep “peak season” since all analyses seemed to have focused on\npeak season.\n3. Please give the rationale for excluding extreme El Niño years from the analysis and briefly discuss and explicitly show its\npotential impact.\n4. L146–151: Clarify that the anticyclonic flow over the northeast Pacific produces westerly anomalies on its equatorward\nflank, vectorially opposing the northeasterly trades since this could not be seen from Supplementary Fig. 5a.\n5. Lines 50-51 “While the impacts of shifting internal natural climatic oscillations on typhoon activity are often overlooked” is\nnot true. There have been many studies that investigated the impacts of various internal long-term climate modes on typhoon\nactivity, including PDO, IPO, AMO, etc. The authors should mention those here.\n6. Although references are comprehensive, some recent work on cross-basin teleconnections where relevant should be\nincluded.\n7. Line 58, remove “attribution of”.\n8. Line 537, change “exerts” to “exert”."}, "author_response": "Response to Reviewer’s comments\nWe are grateful for the time and effort that the reviewers and the editorial team\ndedicated to evaluating our work. Accordingly, we have carefully revised the\nmanuscript by taking all comments and suggestions into account in the revision. Our\npoint-by-point responses to these comments are summarized as follows. Our response\nis marked with “Blue”. Meanwhile, we give the tracked-changes version of the\nmanuscript. Note that the Line numbers and Fig./Table numbers in this response are\ncorresponded to the track-changed version of the revised manuscript.\nREVIEWER COMMENTS\nReviewer #1 (Remarks to the Author):\nReview of Manuscript \"A New Climate Oscillation Takes Typhoon Activity\nToward Asian High-Latitude Cities\"\nThis manuscript presents a novel climate oscillation, the tropical-leaning North\nAtlantic Oscillation (TNAO), and explores its predictive capability for the poleward\nmigration of typhoon activity in East Asian high-latitude cities. The study makes a\nsignificant contribution to the field by identifying a distinct oscillation from the\nclassical North Atlantic Oscillation (NAO) and demonstrating its robust predictive\nskill for typhoon tracks. The findings are particularly relevant for improving seasonal\nforecasts and disaster preparedness in the region under the background of global\nclimate change.\nThe identification of the TNAO as a distinct oscillation from the NAO is a major\ncontribution. The results and conclusions in study are basically convincing. Also, the\nuse of multiple datasets (e.g., ERA5, CMIP6) and statistical techniques (e.g., EOF\nanalysis, wave activity flux) strengthens the credibility of the findings. The exclusion\n1 / 63\nof extreme El Niño years enhances the robustness of the TNAO-TTD relationship.\nThis study clearly links TNAO to jet stream variability, SST anomalies (PMM, NAT),\nand subsequent typhoon steering flows. The cross-basin teleconnection mechanism is\nwell-articulated.\nGenerally, this study makes a compelling case for the TNAO as a key driver of\ntyphoon variability. With minor revisions to clarify mechanisms and contextualize\nfindings, it would be a valuable contribution to high-impact journals. The reviewer\nrecommend acceptance after addressing the above points.\nResponse: We sincerely appreciate the reviewer’s positive and encouraging\ncomments. We are grateful for the constructive suggestions for improvement. We have\ncarefully revised the manuscript to further clarify the physical mechanisms and\nenhance the discussion of the results as suggested. Please check our response below.\nMajor comments:\n(1) While the distinction from NAO is emphasized, further discussion is needed on\nwhy TNAO was not identified earlier. A spectral analysis or comparison with other\nknown modes (e.g., Atlantic Multidecadal Oscillation) could solidify its uniqueness.\nResponse: The reviewer's suggestions were very meaningful. We discussed them\naccordingly and added TNAO and wavelet coherence analysis of multiple modalities\n(Fig. R1.1), as follows:\nLines 76–84: “There are several points need to be emphasized: First, due to the\ngreat fame and wide-spread climate impacts of the extra-tropical NAO, it stealthily\nshields other tropical–extra-tropical atmospheric oscillation. In addition, the TNAO\nappears only during cold seasons (Supplementary Fig. 2) when the NAO is most\nrobust, but substantially weakens and even disappears during warm seasons when\nNAO is also dominant. In other words, only when the NAO signal is removed, can the\ncross-latitudinal correlation of sea level pressure (SLP) directly capture the\npreviously-overlooked TNAO signal (Fig. 1a). This may be the reason why the\n2 / 63\nTNAO was not identified earlier.”\nLines 135–137: “Besides, a comparison with other known decadal modes (e.g.,\nAMO and PDO) via the wavelet coherency spectral analysis shows that the TNAO is\nindeed distinct from the NAO (Supplementary Fig. 4), further solidifying its\nuniqueness.”\nFig. R1.1 (i.e., Supplementary Fig. 4). Wavelet coherence analysis of multiple\nmodes. Wavelet coherence spectral analysis a between TNAO index and AMO index,\nb between TNAO index and PDO index, c between NAO index and AMO index, and\nd between NAO index and PDO index, respectively. Contours denote passing the 95%\nconfidence level. Vectors denote the phase relationship of two indices.\n(2) Please clarify whether TNAO is independent of ENSO on longer timescales, as\nonly extreme El Niño years were excluded.\nResponse: We appreciate the reviewer’s insightful comment. Indeed, examining\n3 / 63\nthe potential influence of ENSO is important. Therefore, we provided scatter plots\nbetween Niño3.4 and TNAO under different conditions (Fig. R1.2). On longer\ntimescale of 1958–2023, TNAO is still independent of ENSO (Fig. R1.2a–b).\nSimilarly, during 1979–2023, relationship between Niño3.4 and TNAO show weak\ncorrelations (Fig. R1.2c–f). After removing ENSO years, the correlation between\nTNAO and TTD remains significant (R = 0.72, p < 0.01; Fig. R1.2f). These results\nsuggest that our main conclusion of the TNAO–TTD correlation is largely\nindependent of ENSO. Combined with the comments of other reviewers on ENSO,\nwe make the following general modifications:\nLines 149–153: “Given that relationship between TNAO and ENSO is not\nsignificant (even in a longer period of 1958–2023; Supplementary Fig. 6a–e), we\nhave tested and found that even in the case of excluding all ENSO years, the\nTNAO-TTD relationship still keeps robust (R = 0.72, p < 0.01; Supplementary Fig.\n6f–g). Accordingly, the cross-seasonal TNAO-TTD linkages are generally\nindependent of ENSO.”\na All years 1958-2023 b All years 1958-2023 c All years 1979-2023\n3 3 3\n2 2 2\n)1 -FJD\n( 4 .3 o ñ iN - 0\n1\n1\n)S A\nJ(\n4 .3 o ñ iN- 0\n1\n1\n)1 -FJD\n( 4 .3 o ñ iN - 0\n1\n1\n-2 -2 -2\nR = -0.09, p = 0.46 R = 0.22, p = 0.08 R = -0.19, p = 0.23\n-3 -3 -3\n-3 -2 -1 0 1 2 3 -3 -2 -1 0 1 2 3 -3 -2 -1 0 1 2 3\nTNAO (MAM) TNAO (MAM) TNAO (MAM)\nd All years 1979-2023 e Exclude extreme El Niño years f Exclude El Niño and La Niña years\n3 3 3\n2 2 2\n)S\nA\nJ(\n4 .3 o ñ iN-1 0\n1 )S\nA\nJ(\n4 .3 o ñ iN-1 0\n1\n)S\nA\nJ( D T T-1 0\n1\n-2 -2 -2\nR = 0.28, p = 0.07 R = 0.18, p = 0.25 R = 0.72, p = 1.3×10−7\n-3 -3 -3\n-3 -2 -1 0 1 2 3 -3 -2 -1 0 1 2 3 -3 -2 -1 0 1 2 3\nTNAO (MAM) TNAO (MAM) TNAO (MAM)\nFig. R1.2 (i.e. part of Supplementary Fig. 6). Relationship between ENSO and\nTNAO. Scatter plot of spring TNAO versus pre-winter (a) and peak-season (b)\n4 / 63\nNiño3.4 indices during 1958–2023. c–d Same as a–b but during 1979–2023. e Same\nas d but excluding 1982, 1997 and 2015. e Same as d but further excluding extreme\nLa Niña years of 1988, 1999 and 2010 (selected by three lowest Niño3.4 index years).\nf Scatter plot of spring TNAO versus TTD excluding El Niño and La Niña years that\nNiño3.4 index exceeding 1.0 absolute value. Correlation coefficients and their\np-values are marked.\n(3) The Gill-type Rossby wave response and \"subtropical Atlantic-Pacific\nteleconnection\" require more detailed explanation or references. A schematic diagram\ncould aid understanding.\nResponse: Here we added detailed explanation (Fig. R1.3-R1.4) and a schematic\ndiagram (Fig. R1.5). Please check the following mechanism:\nLines 197–207: “Due to the TNAO-promoted summer tropical SST changes,\nsignificant atmospheric response can be induced over the WNP, especially the\nlower-level WNP cyclonic circulation anomaly (streamlines in Fig. 3a). Moreover,\npositive PMM linked to TNAO can promote active convection over the subtropical\nNorth Pacific (Supplementary Fig. 11), inducing a Rossby wave cyclone over the\nWNP. Moreover, the lower-level WNP cyclonic circulation can be also identified as\nthe WNP branch of the Pacific–Atlantic subtropical oscillation (PASO)42 driven by the\nTNA SST anomalies via inducing subtropical Walker-cell-like changes\n(Supplementary Fig. 12) and Pacific convection response (Supplementary Figs. 11b\nand d), which further contribute to WNP cyclone44,45.”\nLines 370: “A schematic diagram is presented in Fig. 6.”\n5 / 63\nFig. R1.3 (i.e., Supplementary Fig. 11). TNAO-associated atmospheric anomaly. a\nRegression of 850-hPa horizontal wind (vector) and correlation of precipitation\n(shading) during March–May onto TNAO index. Red rectangle is the same as in\nSupplementary Fig. 9. b Same as a but variables are during July–September. c–d\nSame as a–b but for 200-hPa horizontal wind and outgoing longwave radiation. All\nthe shadings and vectors are significant at the 95% confidence level.\n6 / 63\nFig. R1.4 (i.e., Supplementary Fig. 12). TNA-associated atmospheric anomaly. a\nTime series of TNA (blue) and PASO (red) during July–September from 1982 to 2023.\nCorrelation between them are marked. Note that after removing the long-term trend,\ncorrelation coefficient remains as high as 0.66 (p < 0.01). b Regression of SLP onto\ninverse TNA (contour; interval: 0.2 hPa) and inverse PASO (shading). c Regression of\nvertical circulation (streamline) and corresponding vertical velocity (shading) between\n10°N–30°N onto reverse TNA index during July–September. Hatching indicates\nshading passes the 95% confidence level.\n7 / 63\nFig. R1.5 (i.e., Fig. 6). Schematic diagram illustrating the influence of the TNAO\non WNP typhoons. During spring, the TNAO weakens trade winds over the\nsubtropical northeastern Pacific, leading to local SST warming and subsequent\ndevelopment of the PMM through the seasonal footprint mechanism. Concurrently,\nTNAO strengthens the trade winds over the TNA, resulting in SST cooling that\npersists into summer via local air-sea coupling. The cold TNA SST anomalies can\ncontinuously induce Rossby wave anticyclone to its west, thereby further promoting\nPMM development. The Atlantic-eastern Pacific Rossby wave anticyclone has been\ndemonstrated to directly favor the WNP cyclone via Pacific convection, a\nphenomenon that is explained by the PASO. Consequently, the presence of a\nlower-level cyclone and an upper-level anticyclone in the WNP leads to an eastward\nshift in the TUTT and MT (solid line for positive TNAO and dashed line for negative\nTNAO), promoting more (less) frequent typhoon genesis in the eastern (western)\nWNP. The altered steering flow favors (inhibits) typhoon movement towards higher\n(lower) latitudes of East Asia.\n(4) please address how TNAO-induced SST anomalies persist into summer. Is this due\nto ocean memory or atmospheric feedback?\nResponse: TNAO-induced SST anomalies can continue to develop (PMM) and\nmaintain (NAT) through coupling with the atmosphere. We supplemented the changes\n8 / 63\nin the climate state wind field (Fig. R1.6) to correspond to the changes in the SST\nmode (Fig. R1.7) and explained the mechanism in more detail:\nLines 179–189: “The anticyclonic flows can weaken local trade winds and warm\nSST in the subtropical eastern North Pacific (red rectangle in Supplementary Fig. 9),\nwhich subsequently drives PMM development via the seasonal footprint\nmechanism25,33. On the other hand, the TNAO can induces the NAT SST anomalies\n(Supplementary Fig. 8a). Specially, the TNAO-related anomalous northeasterly winds\nover the tropical North Atlantic (TNA) can enhance the trade winds to cool SST there\n(blue rectangle in Supplementary Fig. 9). This cold SST can further strengthen the\nanticyclone over the eastern Pacific–North Atlantic via the Gill-type Rossby wave\nresponse34,35. Therefore, this cold SST can persist into the following summer\n(Supplementary Fig. 8c) and further contribute to the development of PMM\n(Supplementary Fig. 8d) through a Gill-type Rossby wave response with an\nanticyclone to its west33,36-38.”\nFig. R1.6 (i.e., Supplementary Fig. 9). Climatology of SST and 850-hPa horizontal\nwind over the Pacific and the Atlantic during March–May. Red (blue) vectors indicate\nthat climatic wind will enhance (weaken) associated with positive TNAO (diagnosed\nby climatic wind speed compared with wind speed plus the regression onto TNAO\nindex). Black contour in the Pacific is the 0.5 correlation coefficient of SST onto the\nJuly–September PMM index, representing the origin of PMM. Red and blue\n9 / 63\nrectangles show the regions of PMM development and NAT maintenance,\nrespectively.\nFig. R1.7 (i.e., Supplementary Fig. 8). SST evolution related to TNAO. a\nCorrelation pattern of SST in April–June (shading) and 850-hPa horizontal wind in\nMarch–May (vector) onto the spring TNAO index. b Lead-lag correlation of seasonal\nPMM onto the spring TNAO index. The horizontal dashed line denotes the threshold\nof the 95% confidence level. c–d Same as b but for seasonal NAT and spring TNAO\nindex, and seasonal PMM and April–June NAT index, respectively.\n(5) about the CMIP6 Projections: the 39.4% increase in positive TNAO events is\nintriguing but lacks discussion on model spread or uncertainties. How do model\nbiases (e.g., in Atlantic SST) affect this projection?\nResponse: We agree that the uncertainty of model results is vital. We have\ndiscussed the cold biases in Atlantic SST, which may indeed introduce some\n10 / 63\nuncertainty into future projections for the TNAO. However, current model results\nshow high consistency, leading us to conclude that the reliability of future TNAO\nprojections remains strong. The relevant modifications are as follows:\nLines 343–353: “While we suggest that CMIP6 models show a robust projection\nof more frequent positive TNAO events, some uncertainties remain. The spread\namong models primarily arises from differences in simulating the mean-state and\nvariability of North Atlantic SSTs, as well as the subtropical jet response61,62. It is\nreported that most CMIP6 models exhibit cold SST biases in the North Atlantic,\nespecially in the tropics63,64. This may cause an overestimation of positive TNAO\nfrequency because positive TNAO corresponds to negative TNA SST anomaly.\nNevertheless, the overall positive shift in TNAO occurrence is consistent across most\nmodels, indicating that the projected intensification of positive TNAO is a robust\nmulti-model signal rather than an artifact of a few outliers. Future work could further\nassess the sensitivity of TNAO projections to Atlantic SST biases and internal\nvariability through targeted model experiments.”\nOther comments and suggestions:\n(1) the statement on decelerated typhoon motion seems tangential; either expand or\nomit.\nResponse: Done as suggestion. We intend to highlight that the slowing\nmovement speed of typhoons can lead to an extended duration of their impact, thereby\nunderscoring the growing threat posed by typhoons moving northward in the future.\nWe added more explanation as follows:\nLines 296–300: “In addition, the future anthropogenic warming would decelerate\ntyphoon motions near high-latitude populated coastal regions due to a poleward shift\nof the subtropical westerlies, which can induce a longer time of typhoon impacts55.\nAnd accompanying with increasing intensity and rainfall56, this can potentially\ncompounds future typhoon-related damages.”\n11 / 63\n(2) The abstract overstates \"inevitably face more serious threats.\" Modify to reflect\nuncertainties in CMIP6 projections.\nResponse: Done as suggestion. We revised the sentence as follows:\nLines 33–36: “Climate models project an increasing frequency of positive TNAO\nevents. This implies that East Asian high-latitude cities would potentially face more\nserious typhoon threats as climate warms, yet uncertainty remains due to model biases\nin simulating Atlantic sea surface temperature.”\n(3) Discuss how TNAO’s predictive skill compares to existing indices (e.g., PDO,\nAMO).\nResponse: The reviewer's suggestions warrant careful analysis. We have\ncompiled correlation coefficients between key SST modes during the winter and\nspring seasons and the TNAO and TTD (Table R2.1). As shown, none of these SST\nmodes exhibit significant linear correlations with either the TNAO or TTD.\nThis indicates that the influence of TNAO on TTD operates independently of these SST\nmodes, highlighting the unique predictive role of TNAO. The corresponding\nmodifications are as follows:\nLines 285–293: “In addition to the above climate variables, SST variability often\nhas a significant influence on typhoons. However, unlike metrics such as typhoon\ngenesis location, frequency or intensity47-52, the relationship between other SST\nvariability and TTD remains unexplored. Therefore, we further calculate the\ncorrelation coefficients between the winter-spring SST indices and the TNAO as well\nas the TTD. As shown in Supplementary Table 2, there was no significant linear\ncorrelation between TNAO (or TTD) and other existing indices (Niño3.4, PDO, AMO\nand Indian Ocean Dipole). This suggests that the influence of the TNAO on the TTD\nis generally independent of these SST modes. These findings also highlight the unique\npredictive significance of the TNAO for the northward shift of East Asian typhoons.”\n12 / 63\nTable R1.1 (i.e., Supplementary Table 2). Relationship between preceding\nwinter/spring SST modes and TNAO/TTD. Correlation coefficients of preceding\nwinter (December–January–February, DJF) and spring (March–April–May, MAM)\nNiño3.4, AMO, PDO, Indian Ocean Dipole (IOD) with spring TNAO and peak season\n(July–August–September, JAS) TTD during 1979–2023. None of these correlations is\nsignificant at the 90% confidence level of Student’s t-test.\nSeason of SST indices R Niño3.4 AMO PDO IOD\nTNAO_MAM −0.19 0.15 0.01 −0.16\nDJF\nTTD_JAS −0.12 0.03 0.00 −0.05\nTNAO_MAM −0.12 −0.18 0.19 −0.18\nMAM\nTTD_JAS −0.04 −0.13 0.11 −0.08\n(4) Highlight limitations, such as the assumption of stationary TNAO-typhoon\nrelationships under climate change.\nResponse: Done as suggestion. Discussion regarding possible change\nTNAO-TTD relationship is added:\nLines 326–335: “It should be noted that our hypothesis is based on the\nassumption that the relationship between the TNAO and TTD remains unchanged.\nAdditionally, we suggest that the TNAO-TTD relationship is unchanged after\nexcluding the long-term trends (Supplementary Figs. 14–15). However, under the\ncontext of global warming, typhoon activity is evolving. For instance, the frequency\nof future typhoons may continue to decrease with global warming57-60, which could\nalter the response of typhoon to TNAO forcing. On the other hand, the influence of\nTNAO on typhoons depends on teleconnection processes between the ocean and\natmosphere, which may also change with global warming, thereby altering the\nTNAO-TTD relationship. Therefore, the conjecture presented in this study remains\nhighly preliminary, and further research is needed.”\n13 / 63\nReviewer #2 (Remarks to the Author):\nComments on “A new climate oscillation takes typhoon activity toward Asian\nhigh-latitude cities” by Wu et al.\nThis study focuses an important issue of atmospheric mode on NWP typhoons.\nIn general, this manuscript is well written and shows a clear story line of how TNAO\nimpacts NWP typhoons. However, I really concern the novelty and robustness, which\ncould need extra analyses.\nResponse: We sincerely thank the reviewer for the insightful comments on our\nmanuscript. Accordingly, we have conducted additional analysis to highlight the\nnovelty and robustness of this study, which can substantially improve the quality of\nthe manuscript. Please check our point-to-point response below.\nMajor Comments:\n1. The authors show the TNAO impact on tropical cyclone over the NWP. However, I\nfind that the pathway TNAO impact is much similar to the North Tropical Atlantic\n(NTA) SST impact on Pacific climate (Ham et al. 2013). In this sense, I considered\nthat the TNAO and NTA SST are air-sea coupled system that they share the same\nphysical process affecting the NWP Typhoon. Moreover, the TNAO is relatively\ntransient compared with the NTA and the statistical covariance is fragile. Therefore,\nthe verification of TNAO impact on NWP typhoon activity in numerical models is of\ngreat importance.\nReference:\nHam et al. Sea surface temperature in the north tropical Atlantic as a trigger for El\nNiño/Southern Oscillation events\nResponse: The reviewer’s perspective is highly significant, and we also agree\nthat the tropical North Atlantic (TNA) SST and TNAO share common physical\nprocesses. Just as proposed by Ham et al. (2013), negative TNA SST anomalies first\ninduce an anomalous Rossby anticyclone in its western flank, which subsequently\n14 / 63\ninfluences the development of North Pacific PMM. This process has been\ndemonstrated in their numerical experiments.\nOur mechanism shares similarities: the positive TNAO-related anticyclone (i.e.,\nthe southern part of TNAO) triggers the PMM development in the North Pacific since\nspring, whereas the spring TNAO-induced TNA SST anomalies persist to summer and\nthereby influence North Pacific climate in the following seasons (Fig. 3a). In other\nwords, this indicates that the TNAO exerts a more direct impact on the North Pacific\nclimate, suggesting it may be a superior and earlier typhoon TTD forecasting factor\ncompared to the TNA.\nNevertheless, we also compare it with the influence of TNA on typhoons. As\nshown in Figs. R2.1a-b, the TNA-related typhoon anomalies are markedly weaker\nthan those associated with TNAO. Although TNA also leads to late-stage PMM-like\nSST development (Figs. R2.1c-d), the associated cyclonic anomalies in the WNP are\nmuch weaker than those driven by TNAO (compared to Fig. 3c). However, the\nreviewer’s perspective provided a valuable insight: Given that the TNA influence\nmechanism has been supported by numerical models (Ham et al. 2013), the TNAO\nimpact on Pacific climate is likewise reliable. In other words, the numerical\nexperiments of Ham et al. (2013) can support our mechanism.\nOn the other hand, although the TNAO itself is transient, its effects on the TNA\noperate through the ocean memory, which can develop and persist into the typhoon\npeak-season. Thus, the short-lived nature of TNAO on its own timescale does not\naffect its relationship with typhoons. Accordingly, we have revised the mechanism in\ndetails (Figs. R2.2–2.3) and added related discussion as follows:\nLines 176–196: “Mechanisms of TNAO performance in predicting TTD-like\ntyphoon activity can be explained as follows. On the one hand, the TNAO can\npromote the Pacific Meridional Mode (PMM) SST anomalies through the anticyclonic\nflows over the northeastern Pacific (Supplementary Figs. 8a–b). The anticyclonic\nflows can weaken local trade winds and warm SST in the subtropical eastern North\n15 / 63\nPacific (red rectangle in Supplementary Fig. 9), which subsequently drives PMM\ndevelopment via the seasonal footprint mechanism25,33. On the other hand, the TNAO\ncan induces the NAT SST anomalies (Supplementary Fig. 8a). Specially, the\nTNAO-related anomalous northeasterly winds over the tropical North Atlantic (TNA)\ncan enhance the trade winds to cool SST there (blue rectangle in Supplementary Fig.\n9). This cold SST can further strengthen the anticyclone over the eastern Pacific–\nNorth Atlantic via the Gill-type Rossby wave response34,35. Therefore, this cold SST\ncan persist into the following summer (Supplementary Fig. 8c) and further contribute\nto the development of PMM (Supplementary Fig. 8d) through a Gill-type Rossby wave\nresponse with an anticyclone to its west33,36-38. And the physical process from\nTNA SST to PMM has been demonstrated by Ham et al.37 through observations and\nnumerical experiments. However, the impact of TNAO on TTD is much more\npronounced than that of TNA SST (Supplementary Fig. 10), where only the\nnorthward-moving typhoon is significantly altered40,41. This may be because that\nTNAO not only affects Pacific climate through the persistent TNA SST anomalies in\nlater seasons but also directly triggers the formation of the early prototype of PMM\nvia the southern part anticyclonic circulation of TNAO during spring.”\n16 / 63\nFig. R2.1 (i.e., Supplementary Fig. 10). TNA SST and typhoon activity. a\nRegressions of peak season typhoon genesis density onto the spring Tropical North\nAtlantic (TNA) SST index during 1979–2023. b Same as a but for track density. c\nCorrelation of SST (shading) and regression of 850-hPa horizontal wind (vector; p <\n0.05) during spring (March–April–May) onto the spring TNA index (mean SST in\n80°W–20°E, 0°–20°N). d Same as c but SST and horizontal wind are during peak\nseason (July–August–September).\n17 / 63\nFig. R2.2. (i.e., Supplementary Fig. 8). SST evolution related to TNAO. a\nCorrelation pattern of SST in April–June (shading) and 850-hPa horizontal wind in\nMarch–May (vector) onto the spring TNAO index. b Lead-lag correlation of seasonal\nPMM onto the spring TNAO index. The horizontal dashed line denotes the threshold\nof the 95% confidence level. c–d Same as b but for seasonal NAT and spring TNAO\nindex, and seasonal PMM and April–June NAT index, respectively.\n18 / 63\nFig. R2.3. (i.e., Supplementary Fig. 9). Climatology of SST and 850-hPa horizontal\nwind over the Pacific and the Atlantic during March–May. Red (blue) vectors indicate\nthat climatic wind will enhance (weaken) associated with positive TNAO (diagnosed\nby climatic wind speed compared with wind speed plus the regression onto TNAO\nindex). Black contour in the Pacific is the 0.5 correlation coefficient of SST onto the\nJuly–September PMM index, representing the origin of PMM. Red and blue\nrectangles show the regions of PMM development and NAT maintenance,\nrespectively.\n2. The authors considered the poleward migration of NWP typhoon under global\nwarming. However, many studies have shown that the NWP typhoon frequency will\ndecrease under the global warming (Knutson et al. 2020). The CMIP6 models\ncan only reflect the TNAO changes, however, the connections between NWP typhoons\nand TNAO may also change. The changed NWP typhoons may also shape the NWP\ntyphoon – TNAO relationship.\nReference:\nKnuston et al. Tropical Cyclones and Climate Change Assessment: Part II: Projected\nResponse to Anthropogenic Warming\nResponse: We fully agree with the reviewer's perspective. This point had\npreviously escaped our consideration, so we have revised our original statement to\ndiscuss the potential changes in the relationship between TNAO and typhoons and\npresent our speculations with greater caution. The relevant revisions are as follows:\nLines 322–331: “It should be noted that our inference is based on the premise that\nthe linkage between the TNAO and TTD remains unchanged. Since the cross-seasonal\nlinkage between the two keeps unchanged even after excluding the long-term trends\n(Supplementary Figs. 15–16). However, under the context of global warming,\ntyphoon activity is evolving. For instance, the frequency of future typhoon may\ncontinue to decrease with global warming59-62, which could alter the response of\ntyphoons to TNAO forcing. On the other hand, the influence of TNAO on typhoons\n19 / 63\ndepends on teleconnection processes between the ocean and atmosphere, which may\nalso change with global warming, thereby altering the TNAO-TTD relationship.\nTherefore, the conjecture presented in this study remains highly preliminary, and\nfurther research is needed.”\nFig. R2.4 (i.e., Supplementary Fig. 15). TNAO modulations on typhoon and\ncirculation. Same as Fig. 2a–b, 3a–c, but trend is removed. a Scatter diagram of\nthe spring TNAO (red) and extratropical NAO (blue) against the TTD of peak season\ntyphoon track density. Correlation coefficient (R) between the TNAO (extratropical\n20 / 63\nNAO) and TTD is marked. Straight Lines –are linear least-square fits. b Regressions\nof peak season typhoon track density onto the TNAO (shading) and TTD (contours)\nindices. c Shading in the Pacific (Atlantic) is the correlation pattern of SST in July–\nSeptember onto the simultaneous PMM index (April–June NAT index). StreamLines\n–are the regressed 850-hPa horizontal wind onto the PMM+NAT index (WNP region).\nContours indicate three atmospheric systems: MT (represented by the relative\nvorticity of 3×10−6 s−1 at 850 hPa), TUTT (represented by the 12420-gpm\ngeopotential height at 200 hPa) and South Asian high (SAH; represented by the\n12500-gpm geopotential height at 200 hPa). Solid (dashed) contours are calculated as\nthe climatology plus (minus) the double regressions onto the PMM+NAT index. d\nRegressions of peak season Genesis Potential Index (shading) and typhoon genesis\ndensity (contour; 10−1 yr−1) on the spring TNAO index. e Regressions of peak season\n700-hPa geopotential height (shading) and steering flow (vector; p < 0.05) onto the\nspring TNAO index. Hatching indicates that it passes the 95% confidence level.\n21 / 63\nFig. R2.5 (i.e., Supplementary Fig. 16). SST evolution related to TNAO. Same as\nSupplementary Fig. 5, but trend is removed. a Correlation pattern of SST in April–\nJune (shading) and 850-hPa horizontal wind in March–May (vector) onto the spring\nTNAO index. b Lead-lag correlation of seasonal PMM onto the spring TNAO index.\nThe horizontal dashed line denotes the threshold of the 95% confidence level. c–d\nSame as b but for seasonal NAT and spring TNAO index, and seasonal PMM and\nApril–June NAT index, respectively.\n3. The authors only considered the typhoons, which reach 32.6m/s and above, right?\nHowever, I believe that the TNAO could affect both typhoons and tropical cyclones\n(17-32.6 m/s) in the NWP. The authors need to show the TNAO impact on all TC\nfrequency and explain why they only focused on typhoons?\nResponse: We appreciate the reviewer’s comment. Following the suggestion,\nwe have examined the relationship between the TNAO and the frequency of all tropical\ncyclones (TCs, ≥ 17.2 m s⁻¹) in the Northwest Pacific (Fig. R2.6). The result confirms\nthat the TNAO significantly modulates overall TC East Asian dipole mode, with a\ncorrelation of R = 0.60. This is consistent with the strong influence found for\ntyphoons (≥ 32.6 m s⁻¹), for which the correlation is higher (R = 0.75). These analyses\ndemonstrate that the TNAO exerts robust impacts across the full spectrum of TC\nintensities.\nOur focus on typhoons was motivated by their disproportionately large societal\nand climatic impacts compared with weaker TCs, including more severe damages,\neconomic losses, and fatalities (e.g., Knutson et al., 2020; Zhai et al., 2014;).\nTherefore, highlighting typhoons emphasizes the relevance of the TNAO to high‐\nimpact events. Nevertheless, we agree that the TNAO is also a useful predictor of\noverall TC variability, and we now clarify this point in the revised manuscript as\nfollows:\nLines 390–394: “We focus on the East Asia–WNP typhoon (TC with V > 32.6\nmax\nm s−1) during July–September of 1979–2023, using the Joint Typhoon Warning Center\n22 / 63\nTC best-track datasets. Although TNAO exerts some influence on typhoon activity in\nother months, its relationship with the TTD mode is mainly concentrated in the peak\nseason (i.e., July-September) when WNP typhoon most active (Supplementary Fig.\n22). Thus we mainly focus on July–September. This study primarily focuses on\ntyphoons because stronger typhoons are more likely to cause significant damage60,75.\nWe also examined results for all TCs with V > 17.2 m s−1, which showed consistent\nmax\nfindings (Supplementary Fig. 23), although the relationship with TNAO may be\nsomewhat weaker for weaker TCs due to their inherent randomness.”\nFig. R2.6 (i.e., Supplementary Fig. 22). Relationship between TNAO and all TC\nactivity during peak season. Same as Fig.2a–b but for TCs with a V > 17.2 m s−1.\nmax\nReferences:\nKnutson, T., Camargo, S. J., Chan, J. C., Emanuel, K., Ho, C. H., Kossin, J., ... & Wu,\nL. (2020). Tropical cyclones and climate change assessment: Part II: Projected\nresponse to anthropogenic warming. Bulletin of the American Meteorological\nSociety, 101(3), E303-E322. https://doi.org/10.1175/BAMS-D-18-0194.1\nZhai, A. R., & Jiang, J. H. (2014). Dependence of US hurricane economic loss on\nmaximum wind speed and storm size. Environmental Research Letters, 9(6),\n23 / 63\n064019. https://doi.org/10.1088/1748-9326/9/6/064019\nMinor comments:\n1. Line 299: The definition of jet shift index could be incorrect. Please modify the\ndefinition region. Also, it is worthy noted that the authors defined too many\natmospheric modes in the Method part, I suggested to revise the definitions and\nexplain the purpose of the usage.\nResponse: We appreciate the reviewer for pointing out this issue: the original\ndefinition of jet shift index was indeed not reasonable enough. We have revised this\nindex by expanding its definition regions (as shown by the boxes in Fig. R2.7a).\nThis modification allows the jet shift index to capture the meridional variation of the\njet axis (Fig. R2.7b) and slightly improving the correlation coefficient with the\nTNAO (from 0.83 to 0.85).\nFurthermore, the original manuscript contained multiple index definitions, which\nmay have appeared confusing. To present the definitions more clearly, we have\nconsolidated them into the Table R2.1 and revised the manuscript as follows:\nLines 115–117: “Moreover, the TNAO index has a robust Pearson correlation\ncoefficient up to 0.85 (p < 0.01) with the North America-Atlantic jet meridional shift\nindex (NAJS; see Methods, Fig. 1c and Supplementary Fig. 3).”\nLines 420–430: “This paper employs multiple indices, primarily atmospheric\nmodes and SST indices, with specific definitions provided in Supplementary Table 3.\nWe define the spring (March–May) TNAO and NAO indices based on the difference\nin SLP between two regions. We compare our NAO index with downloaded NAO\nindex from\nhttps://climatedataguide.ucar.edu/climate-data/hurrell-north-atlantic-oscillation-NAO-\nindex-pc-based, which is the leading EOF mode of SLP in (90°W–40°E, 20°N–80°N),\nand find that two time series show strong Pearson correlation (R = 0.97, p < 0.001)\nduring spring. The SST index is defined as the regional average sea surface\ntemperature. We utilized multiple atmospheric or high-latitude modal indices from\n24 / 63\npreceding winter and spring seasons (with specific seasons determined by high\ncorrelation coefficients in Supplementary Fig. 25) to highlight the unique and\ndominant role of TNAO.”\nFig. R2.7 (i.e., Supplementary Fig. 3). Definition of the jet shift index. a\nDifference in 200-hPa zonal wind between positive and negative phases of the jet\nmeridional shift index, defined based on one standard deviation. Positive years are\n1979, 1980, 1981, 1983, 1984, 1987, 1988, 1998, 1999, 2005, 2006, 2010, and 2013.\nNegative years are 1985, 1989, 1991, 1994, 2000, 2002, 2004, 2009, 2018, 2019, and\n2022. b Composite of 200-hPa zonal wind speed during positive years (contours and\nthick blue line) and negative years (shading and thick red line). The thick Lines –\nindicate the jet axis, defined as the zero line of the meridional gradient of zonal wind.\nc Time series of TNAO (red), TTD (blue) and NAJS (gray) and correlation\ncoefficients between them.\n25 / 63\nTable R2.1 (i.e. Supplementary Table 3). Definition of indices used in this\nmanuscript. In this study, we use various indices during 1979–2023, including the\nTNAO, NAO, NAT, PMM, TNA, PASO, NAJS, Northern Annular Mode (NAM),\nNorth Pacific Oscillation (NPO), and Arctic sea-ice concentration (SIC). Seasons are\nabbreviated by the first letter of the month. All the subscripts denote the regionally or\nzonally averaged and standardized variables.\nIndex Variable (Season) Definition\nSLP − SLP\n(25°W–105°W, 15°N–35°N) (45°W–95°W, 55°N–\nTNAO Sea level pressure (MAM)\n65°N)\nNAO Sea level pressure (MAM) SLP − SLP\n(60°W–30°E, 40°N) (60°W–30°E, 70°N)\nSea surface temperature SST − SST\n(30°W–50°W, 45°–55°N) (40°W–100°W, 25°N–40°N)\nNAT\n(AMJ) − SST\n(10°W–60°W, 7.5°N–22.5°N)\nSea surface temperature SST + SST\n(160°E–175°W, 8°N–18°N) (110°W–130°W, 14°N–\nPMM\n(JAS)\n20°N)\nSea surface temperature\nTNA SST\n(80°W–20°E, 0°–20°N)\n(JAS)\nFirst leading principal component of SLP\n(110°E–\nPASO Sea level pressure (JAS)\n30°W, 10°N–30°N)\nU200 − U200\n(40°W–105°W, 42.5°N–52.5°N) (40°W–105°W,\nNAJS 200-hPa zonal wind (MAM)\n22.5°N–32.5°N)\nNAM Sea level pressure (MAM) SLP − SLP\n(0°–360°, 45°N) (0°–360°, 80°N)\nNPO Sea level pressure (MAM) SLP − SLP\n(160°E–140°W, 30°N) (170°E–120°W, 65°N)\nSea ice concentration\nSIC SIC\n(160°W–180°, 60°N–68°N)\n(FMA)\n2. It is unfair to move the TNAO signal from the NAO index since they are highly\ncorrelated in Fig.4. The authors could also check the TNAO-TC track correlation\nwhen removing the NAO signal. I believe it is also weak and insignificant.\nResponse: Done as suggestion. Our results indicate that even after linearly\nremoving NAO signal, TNAO shows high connection with the TTD pattern (Fig.\n26 / 63\nR2.8). NAO has a high correlation with typhoons that move northward toward\nnorthern East Asia (Fig. R2.9), therefore removing NAO signal weakens this signal in\nTNAO-related typhoon track pattern (positive correlation in Fig. R2.8d). Still,\ncorrelation pattern of typhoon track density onto TNAO significantly exhibits the\nmeridional dipole, confirming the advance and robustness of TNAO in predicting\nTTD than that of NAO. We revised the manuscript as follows:\nLines 271–280: “We further compare the correlation pattern of Prediction\nTTD\n(Fig. 4b) with other prediction models established only using TNAO (Fig. 4c–d) and\nNAO (Fig. 4e and Supplementary Fig. 19). The Prediction explains 61.7% of the\nTTD\nobserved variances in TTD variations during 1979–2023 (evaluated by the R2\nbetween indices; Fig. 4b), while the TNAO alone explains a substantial 56.3% of the\nvariation (Fig. 4c). When the NAO signal is removed, TNAO maintains its strong\nlinkage to the TTD pattern (Fig. 4d). In contrast, the extratropical NAO-model\n(Supplementary Fig. 19) reflects very low predictability (R2 = 14.7%). Moreover,\nwhen the TNAO signal is removed, the predicted variances from the extratropical\nNAO decreases to approximately 0% (Fig. 4e), suggesting that the relationship\nbetween the extratropical NAO and TNAO is not independent of the TNAO.”\n27 / 63\nFig. R2.8 (i.e., Fig. 4). Predicting Asia–Pacific typhoon activity. a Time series of\nstandardized observed TTD (blue bar) and predicted TTD (red line) during 1979–\n2023. Prediction is divided into training period (1979–2014; blue shading) and\nvalidation period (2016–2023; red shading). The correlation and significance level\nbetween observation and prediction are marked, also the RMSE for two periods. b–e\nCorrelation pattern between typhoon track density and time series predicted by\ndifferent predictors (see Methods for details) in 1979–2023: using TNAO,\nextratropical NAO, NAM, NPO and SIC (b), TNAO only (c), TNAO only and\nexcluding NAO signal (d), NAO only and excluding TNAO signal (e). Hatching\nindicates that the correlation passes the 95% confidence level. The corresponding R2\nbetween TTD index and prediction model is marked.\n28 / 63\nFig. R2.9 (i.e., Supplementary Fig. 19). Same as Fig. R1.4b, but for NAO.\n3. Since the TNAO is induced by the jet shear, I also want to the correlations between\nNorth America jet stream and NWP typhoons (and TNAO).\nResponse: Done as suggestion. In Fig. R2.7, we also show the seasonal\ncorrelation between the North American jet stream and TNAO and TTD. In spring,\nthe North American jet stream has the highest correlation with TNAO and peak\nseason TTD, which are 0.85 and 0.64, respectively (both p < 0.01). We added this\ninformation in the manuscript:\nLines 144–145: “Besides, the NAS has a weaker correlation of 0.64 with TNAO\n(Supplementary Fig. 3c).”\n4. Line 126: why do they authors only remove three strong El Niño events? I believe\nthat the TNAO is highly correlated with the NTA SST simultaneously and\nconsequently ENSO signal. Therefore, it may also need to consider both El Niño and\nLa Niña events.\nResponse: We appreciate the reviewer’s insightful comment. Indeed, examining\n3 / 63\nthe potential influence of ENSO is important. Therefore, we provided scatter plots\nbetween Niño3.4, TNAO, and TTD under different conditions (Fig. R2.10). During\n1979–2023, Niño3.4 and TNAO show a weak correlation (R = 0.28, p = 0.07; Fig.\nR2.10a). After removing three strong El Niño years (1982, 1997, 2015), the\ncorrelation becomes insignificant (R = 0.18, p = 0.25; Fig. R2.10b). When we instead\nremove the three extreme El Niño and La Niña years (1982, 1997, 2015, 1988, 1999,\nand 2007), the correlation of Niño3.4 and TNAO keeps insignificant (Fig. R2.10c),\nbut the TNAO–TTD relationship keeps robust (R = 0.73; Fig. R2.10d), just slightly\nweakens compared to that in the main text (R = 0.75). Furthermore, excluding all El\nNiño and La Niña years (i.e., 1982, 1987, 1988, 1997, 1998, 1999, 2002, 2007, 2010,\n2015, 2022 and 2023) yields a similar result (R = 0.72; Fig. R2.10e), albeit slightly\nlower than 0.75. These results confirm that our main conclusion is robust, ensuring\nthat the TNAO–TTD correlation remains as high as possible while maintaining an\ninsignificant linear relationship between TNAO and Niño3.4. We also examine the\nmultiple regression when excluding both extreme El Niño and La Niña years (Fig.\nR2.11), and found that results are also robust.\nLines 149–153: “Given that relationship between TNAO and ENSO is not\nsignificant (even in a longer period of 1958–2023; Supplementary Fig. 6a–e), we\nhave tested and found that even in the case of excluding all ENSO years, the\nTNAO-TTD relationship still keeps robust (R = 0.72, p < 0.01; Supplementary Fig.\n6f–g). Accordingly, the cross-seasonal TNAO-TTD linkages are generally\nindependent of ENSO signal.”\nLines 267–268: “This prediction is generally independent of ENSO\n(Supplementary Fig. 18).”\n30 / 63\nFig. R2.10 (i.e., part of Supplementary Fig. 6). Relationship between ENSO and\nTNAO. Scatter plot of spring TNAO versus peak-season (a) Niño3.4 index during\n1979–2023. (b) Same as a but excluding years 1982, 1997 and 2015. (c) Same as b\nbut further excluding extreme La Niña years of 1988, 1999 and 2010 (selected by\nthree lowest Niño3.4 index years). (d) Same as c but for TNAO versus TTD. (e)\nScatter plot of spring TNAO versus TTD excluding El Niño and La Niña years that\nNiño3.4 index exceeding 1.0 absolute value. Correlation coefficients and their\np-values are marked.\nFig. R2.11 (i.e., Supplementary Fig. 18). Same as Fig. 4a, but further excluding\nthree extreme La Niña years (1988, 1999 and 2010).\n31 / 63\n5. Line 273 and Line 73: the typhoon season is defined as the months from July to\nSeptember or August? Note, the portion of July-September typhoons could not be a\nmajority for the whole typhoon season tropical cyclone genesis frequency from June\nto October.\nResponse: In this study, we focus on typhoons during July–September. We agree\nthat the portion of July-September typhoons could not be a majority for the whole\ntyphoon season from June to October. However, the meridional dipole in response to\nTNAO does not exist in all months of whole typhoon season, as shown in Fig. R2.12.\nFor the whole season from June to October, the meridional dipole o typhoon track\ndensity linked to TNAO is weaker (Fig. R2.12a versus b). More specifically, the\ndipole with positive anomalies in the northern region and negative anomalies in the\nsouthern region only appears from July to September, while no significant dipole is\nobserved in June, October, or November (Fig. R2.12c-h). Therefore, it is most\nappropriate to designate July to September as the primary seasonal period for TNAO's\ninfluence on TTD. We have incorporated this information into the method section:\nLines 391–394: “Although TNAO exerts some influence on typhoon activity in\nother months, its relationship with the TTD mode is mainly concentrated in the peak\nseason (i.e., July-September) when WNP typhoon most active (Supplementary Fig.\n22). Thus we mainly focus on July–September.”\n32 / 63\nFig. R2.12 (i.e., Supplementary Fig. 22). Seasonal and monthly typhoon anomaly\nin response to TNAO. Correlation of typhoon track density in (a) June–October and\n(b) July–September. Regression of typhoon track density in (c) June, (d) July, (e)\nAugust, (f) September, (g) October and (h) November onto the spring TNAO index\nduring 1979–2023. Rectangles are the same as in Fig. 2b.\n33 / 63\nReviewer #3 (Remarks to the Author):\nSummary: The manuscript identifies and characterizes a novel springtime North\nAtlantic seesaw—termed the Tropical-leaning North Atlantic Oscillation\n(TNAO)—and demonstrates its predictive linkage to summer typhoon-track density\n(TTD) over the western North Pacific via modulation of the Pacific Meridional Mode\n(PMM). By defining a spring sea level pressure (SLP) difference index, the authors\nshow robust correlations (R ≃ 0.75) with the leading empirical orthogonal function\n(EOF) of typhoon tracks, far exceeding the classical NAO’s skill. They propose two\ncross-basin pathways: (1) TNAO-driven anticyclonic flow over the Northeastern\nPacific weakens trades, warms SST, and seeds the PMM; and (2) an Atlantic SST\ntripole forces a Rossby response that reinforces the PMM. CMIP6 projections under\nSSP5-8.5 indicate increases in positive TNAO events, suggesting enhanced future\ntyphoon poleward migration. Overall, the conceptual advance is substantial,\nlinking two ocean basins through a physically plausible teleconnection with seasonal\nforecasting potential.\nGeneral evaluation: This study presents a new cross-basin teleconnection with strong\npredictive implications, making a substantial and well‐validated contribution to our\nunderstanding of cross-basin climate–typhoon linkages and offering a seasonal\nforecasting potential. Pending clarifications on several issues described below and\nsome extended analysis to strengthen the robustness, I recommend acceptance after a\nmajor revision. I believe that once these points are satisfactorily resolved, the paper\nwill be well suited for Nature Communications.\nResponse: We sincerely appreciate the reviewer’s thorough evaluation and\nencouraging remarks. We are grateful for the recognition of our study’s conceptual\nadvance in identifying a novel cross-basin teleconnection with strong predictive\nimplications. We have carefully addressed all the concerns raised and have\nsubstantially revised the manuscript to enhance the robustness, clarity, and overall\n34 / 63\npresentation of our findings. Please check our response below.\nMajor Comments\n1. Asian high-latitude cities: In the title, the authors emphasized the typhoon activity\ntoward Asian high-latitude cities. However, throughout the manuscript, the reviewer\ncould not find any analysis on landfalling typhoons that threatened cities in the\nrelevant regions. The authors should explicitly demonstrate the point by showing the\nincrease in landfall frequency. An analysis of landfall frequency or distance from\ncoast would help clarify the claim.\nResponse: We thank the reviewer for this valuable comment. We agree that the\nsocietal significance of our work lies in the increasing threat of poleward-shifting\ntyphoons to high-latitude Asian cities. In the original manuscript, we have quantified\nthis threat using track density (representing the number of typhoons affecting each\ngrid point) and Accumulated Cyclone Energy (ACE, which further accounts for wind\nintensity). These two metrics already capture the exposure and destructive potential of\ntyphoons for specific cities (see Fig. 2c). Nevertheless, we acknowledge that these\nmetrics may not be as intuitive as landfall frequency. Therefore, in the revised\nmanuscript, we additionally provide the anomaly of typhoon frequency entering two\nrepresentative boxes (see Fig. 2b). We revised related contents as follows:\nLines 163–173: “Moreover, as shown in Fig. 2c, positive TNAO can significantly\npredicts the above-normal typhoon occurrence frequency (measured by track\ndensity) and destructive powers (measured by Accumulated Cyclone Energy32,\nACE, see Methods) of typhoons four months in advance, for East Asian high-latitude\ncities such as Tokyo, Osaka, Kagoshima, Seoul, Qingdao, and Shanghai; meanwhile,\nthe below-normal destructive powers of typhoons for East Asian low-latitude cities\nlike Hong Kong, Manila and Haikou can also be foreseen. We further measure the\ntyphoon frequency in each box in response to TNAO anomaly (see bar in Fig. 2b).\nA one standard deviation change in the TNAO index corresponds to an increase\nof 1.0 ± 0.45 in typhoon frequency in northern East Asia (see red box) and a\n35 / 63\ndecrease of 0.65 ± 0.45 in typhoon frequency in southern East Asia (see blue\nbox).”\nFig. R3.1 (i.e., Fig. 2). Impacts of spring TNAO on summer Asia–Pacific typhoon\nactivity. a Scatter diagram of the spring TNAO (red) and extratropical NAO (blue)\nagainst the TTD of peak season typhoon track density during 1979–2023. Correlation\ncoefficient (R) between the TNAO (extratropical NAO) and TTD is marked. Straight\nLines –are linear least-square fits. b Regressions of peak season typhoon track density\nonto the TNAO (shading) and TTD (contours) indices. Bars in the upper-right corner\nare the regression of typhoon frequency entering northern East Asia (NEA, red box)\nand southern East Asia (SEA, blue box) onto the TNAO index, with the error bar\nshowing the 95% confidence level. c Regressions of peak season local track density\n(blue) and Accumulated Cyclone Energy (red) onto the spring TNAO index for\nNortheast and Southeast Asian regions (marked in b with red and blue boxes,\nrespectively) and nine major cities (marked in b). The error bar is the 95% confidence\n36 / 63\ninterval. Solid (open) circle indicates passing the 99% (95%) confidence level. Three\nextreme El Niño years (1982, 1997 and 2015) are excluded.\n2. Long-term trend vs internal climate variability: Given global warming, both\nAtlantic and Pacific climates have a warming trend. Have the authors removed a\nlinear (or higher‐order) trend from their SLP/SST fields before computing TNAO and\nPMM? Note that a lingering trend could spuriously enhance their teleconnection\nstatistics.\nResponse: The reviewer's perspective is well-founded, and it is crucial to\ninvestigate whether trend effects influence our findings. After removing trends\nthrough linear regression, we recalculated TNAO, TTD, and related\natmospheric-ocean circulation anomalies. The results indicate that trends generally do\nnot affect our conclusions (Figures R3.2–R3.3), with slightly changes observed in the\nrelationship between TNAO and TTD or associated physical processes. As we aim to\nemphasize TNAO's potential role in typhoon northward movement and its possible\nimpacts from future changes, the main text retains the trend-removed results, while\nFigure R3.2–R3.3 serves as a supplementary illustration. The manuscript has been\nrevised as follows:\nLines 242–246: “We further examined the above results after removing the linear\ntrend and found that the relationship between TNAO and TTD is still robust, and the\nrelated atmospheric and oceanic processes are also significant (Supplementary Figs.\n15–16), indicating that the TNAO mode and its climatic influence are not a signal of\nglobal warming trend, but internal climate variability.”\n37 / 63\nFig. R3.2 (i.e., Supplementary Fig. 15). TNAO modulations on typhoon and\ncirculation. Same as Fig. 2a–b, 3a–c, but trend is removed. a Scatter diagram of\nthe spring TNAO (red) and extratropical NAO (blue) against the TTD of peak season\ntyphoon track density. Correlation coefficient (R) between the TNAO (extratropical\nNAO) and TTD is marked. Straight Lines –are linear least-square fits. b Regressions\nof peak season typhoon track density onto the TNAO (shading) and TTD (contours)\nindices. c Shading in the Pacific (Atlantic) is the correlation pattern of SST in July–\nSeptember onto the simultaneous PMM index (April–June NAT index). StreamLines\n–are the regressed 850-hPa horizontal wind onto the PMM+NAT index (WNP region).\n38 / 63\nContours indicate three atmospheric systems: monsoon trough (MT; represented by\nthe relative vorticity of 3×10−6 s−1 at 850 hPa), tropical upper-troposphere trough\n(TUTT; represented by the 12420-gpm geopotential height at 200 hPa) and South\nAsian high (SAH; represented by the 12500-gpm geopotential height at 200 hPa).\nSolid (dashed) contours are calculated as the climatology plus (minus) the double\nregressions onto the PMM+NAT index. d Regressions of peak season Genesis\nPotential Index (shading) and typhoon genesis density (contour; 10−1 yr−1) on the\nspring TNAO index. e Regressions of peak season 700-hPa geopotential height\n(shading) and steering flow (vector; p < 0.05) onto the spring TNAO index. Hatching\nindicates that it passes the 95% confidence level.\nFig. R3.3 (i.e., Supplementary Fig. 16). SST evolution related to TNAO. Same as\nSupplementary Fig. 5, but trend is removed. a Correlation pattern of SST in April–\nJune (shading) and 850-hPa horizontal wind in March–May (vector) onto the spring\nTNAO index. b Lead-lag correlation of seasonal PMM onto the spring TNAO index.\n39 / 63\nThe horizontal dashed line denotes the threshold of the 95% confidence level. c–d\nSame as b but for seasonal NAT and spring TNAO index, and seasonal PMM and\nApril–June NAT index, respectively.\n3. Memory from prior seasons: Spring TNAO events may be influenced by preceding\nwinter’s modes (e.g., ENSO, IOD, PDO, AMO). Ensuring the predictor is not\ncontaminated by autoregressive persistence will clarify the true lead‐time benefits.\nResponse: The reviewer's suggestions are very important. Therefore, we present\nthe correlation coefficients between these SST modes and TNAO and TTD for the\nprevious winter. As shown in Table R3.1, none of these SST modes in preceding\nseason exhibit significant linear correlations with either the TNAO or TTD. This\nindicates that the influence of TNAO on TTD operates independently of these SST\nmodes, highlighting the unique predictive role of TNAO. We discussed this table to\nclarify the true lead‐time benefits:\nLines 285–294: “In addition to the above climate variables, SST variability often\nhas a significant influence on typhoons. However, unlike metrics such as typhoon\ngenesis location, frequency or intensity47-52, the relationship between other SST\nvariability and TTD remains unexplored. Therefore, we further calculate the\ncorrelation coefficients between the winter-spring SST indices and the TNAO as well\nas the TTD. As shown in Supplementary Table 2, there was no significant linear\ncorrelation between TNAO (or TTD) and other existing indices (Niño3.4, PDO, AMO\nand Indian Ocean Dipole). This suggests that the influence of TNAO on the TTD is\ngenerally independent of these SST modes. These findings also highlight the unique\npredictive significance of the TNAO for the northward shift of East Asian typhoons.”\nTable R3.1 (i.e., Supplementary Table 2). Relationship between preceding\nwinter/spring modes and TNAO/TTD. Correlation coefficients of preceding winter\n(December–January–February, DJF) and spring (March–April–May, MAM) Niño3.4,\n40 / 63\nAMO, PDO, Indian Ocean Dipole (IOD) with spring TNAO and peak season (July–\nAugust–September, JAS) TTD during 1979–2023. None of these correlations is\nsignificant at the 90% confidence level of Student’s t-test.\nSeason of SST indices Niño3.4 AMO PDO IOD\nTNAO_MAM −0.19 0.15 0.01 −0.16\nDJF\nTTD_JAS −0.12 0.03 0.00 −0.05\nTNAO_MAM −0.12 −0.18 0.19 −0.18\nMAM\nTTD_JAS −0.04 −0.13 0.11 −0.08\n4. Pacific-basin wind-anomaly: The manuscript shows only the Atlantic (NAT/TNAO)\n850 hPa anomaly map (Supplementary Fig. 5a), but omits the analogous Pacific\nanomaly field demonstrating trade-wind weakening. Please include a spatial map of\n850 hPa wind anomalies regressed onto the PMM (or TNAO) over the Pacific to\nvisually confirm the westerly anomalies in the 8–18° N band.\nResponse: Done as suggestion. Here we provided Fig. R3.4–R3.5 to show the\nweakened trade winds over the subtropical eastern North Pacific (see rectangle in\nfigures), and the anomalous 850 hPa winds. We confirm that there are westerly winds\nfrom the subtropical eastern North Pacific to the WNP that favors the weakened trade\nwinds. We have revised the manuscript to give a more detailed description of the\nrelevant physical processes:\nLines 176–189: “Mechanisms of TNAO performance in predicting TTD-like\ntyphoon activity can be explained as follows. On the one hand, the TNAO can\npromote the Pacific Meridional Mode (PMM) SST anomalies through the anticyclonic\nflows over the northeastern Pacific (Supplementary Figs. 8a–b). The anticyclonic\nflows can weaken local trade winds and warm SST in the subtropical eastern North\n41 / 63\nPacific (red rectangle in Supplementary Figs. 9 and 11a), which subsequently drives\nPMM development via the seasonal footprint mechanism27,35. On the other hand, the\nTNAO can induces the NAT SST anomalies (Supplementary Fig. 8a). Specially, the\nTNAO-related anomalous northeasterly winds over the tropical North Atlantic (TNA)\ncan enhance the trade winds to cool SST there (blue rectangle in Supplementary Fig.\n9). This cold SST can further strengthen the anticyclone over the eastern Pacific–\nNorth Atlantic via the Gill-type Rossby wave response36,37. Therefore, this cold SST\ncan persist into the following summer (Supplementary Fig. 8c) and further contribute\nto the development of PMM (Supplementary Fig. 8d) through a Gill-type Rossby wave\nresponse with an anticyclone to its west34,38-40.”\nFig. R3.4 (i.e., Supplementary Fig. 11). TNAO-associated atmospheric anomaly. a\nRegression of 850-hPa horizontal wind (vector) and correlation of precipitation\n(shading) during March–May onto TNAO index. Red rectangle is the same as in\nSupplementary Fig. 9. b Same as a but variables are during July–September. c–d\nSame as a–b but for 200-hPa horizontal wind and outgoing longwave radiation. All\nthe shadings and vectors are significant at the 95% confidence level.\n42 / 63\nFig. R3.5 (i.e., Supplementary Fig. 9). Climatology of SST and 850-hPa horizontal\nwind over the Pacific and the Atlantic during March–May 1979–2023. Red (blue)\nvectors indicate that climatic wind will enhance (weaken) associated with positive\nTNAO (diagnosed by climatic wind speed compared with wind speed plus the\nregression onto TNAO index). Black contour in the Pacific is the 0.5 correlation\ncoefficient of SST onto the July–September PMM index, representing the origin of\nPMM. Red and blue rectangles show the regions of PMM development and NAT\nmaintenance, respectively.\n5. Confounding climate modes: Other low-frequency modes (AMO, PDO/IPO),\nENSO, and IOD may influence both TNAO and typhoon metrics, potentially biasing\nthe TNAO→TTD link. The authors should perform multivariate or partial-correlation\ntests that control for AMO, PDO/IPO, ENSO (Niño 3.4), and IOD indices and to\nconfirm that TNAO retains unique predictive skill.\nResponse: The reviewer's point is valid. These low frequency modes are\nimportant for typhoons. In our response to Major comment 3, we find that these SST\nmodes has little linear relationship with TNAO or TTD. Thus Table R3.1 confirm that\nTNAO retains unique predictive skill. Please check Major comment 3 for details. As\nshown in Table R3.1, none of these SST modes in preceding season exhibit\nsignificant linear correlations with either the TNAO or TTD. This indicates that the\n43 / 63\ninfluence of TNAO on TTD operates independently of these SST modes, highlighting\nthe unique predictive role of TNAO.\nLines 285–294: “In addition to the above climate variables, SST variability often\nhas a significant influence on typhoons. However, unlike metrics such as typhoon\ngenesis location, frequency or intensity47-52, the relationship between other SST\nvariability and TTD remains unexplored. Therefore, we further calculate the\ncorrelation coefficients between the winter-spring SST indices and the TNAO as well\nas the TTD. As shown in Supplementary Table 2, there were no significant linear\ncorrelation between TNAO (or TTD) and other existing indices (Niño3.4, PDO, AMO\nand Indian Ocean Dipole). This suggests that the influence of TNAO on the TTD is\ngenerally independent of these SST modes. These findings also highlight the unique\npredictive significance of the TNAO for the northward shift of East Asian typhoons.”\nTable R3.1 (i.e. Supplementary Table 2). Relationship between preceding\nwinter/spring SST modes and TNAO/TTD. Correlation coefficients of preceding\nwinter (December–January–February, DJF) and spring (March–April–May, MAM)\nNiño3.4, AMO, PDO, Indian Ocean Dipole (IOD) with spring TNAO and peak season\n(July–August–September, JAS) TTD during 1979–2023. None of these correlations is\nsignificant at the 90% confidence level of Student’s t-test.\nSeason of SST indices R Niño3.4 AMO PDO IOD\nTNAO_MAM −0.19 0.15 0.01 −0.16\nDJF\nTTD_JAS −0.12 0.03 0.00 −0.05\nTNAO_MAM −0.12 −0.18 0.19 −0.18\nMAM\nTTD_JAS −0.04 −0.13 0.11 −0.08\n6. Potential confounding from monsoon and shear: Summer monsoon trough position\n44 / 63\nand vertical wind shear directly modulate both genesis and track of typhoons over the\nwestern North Pacific. Even if PMM explains part of the signal, the residual may\nsimply be a shifted monsoon trough or the Pacific TUTTs. Could the authors provide\nany insights on possible connection? At least, this should be discussed briefly to\ndemonstrate the PMM’s unique pathway as discussed in the manuscript.\nResponse: To provide insights on the connection between the zonal shift of\nTUTT/MT and PMM/NAT, we first calculate the longitude of TUTT and MT (see\ncaption of Fig. R3.7) and then plot the correlation pattern of SST onto the time series\nof TUTT and MT longitude. Correlation between indices are also marked. The\nlocations of TUTT and MT are primarily associated with the central Pacific El Niño\nand tropical Atlantic SST (Fig. R3.7). However, areas where PMM- and\nNAT-correlated SST (contours) and shading partially overlap can be observed,\nresulting in significant zonal correlations (p < 0.01) between these regions and\nTUTT/MT. These findings confirm that TNAO-induced succeeding SST anomalies\ncan indeed trigger zonal shifts in TUTT and MT locations. The article is revised as\nfollows:\nLines 210–213: “Here we further confirm the relationship between TNAO-related\nSST anomalies and the zonal shift of TUTT and MT. The NAT and PMM SST\npatterns can partially contribute to the longitude changes in TUTT and MT since their\ncorrelations are significant (Supplementary Fig. 13).”\nSupplementary Methods: “The TUTT longitude is defined as the western\nboundary of the zero contour of zonal wind speed over 5°N–20°N84. The MT\nlongitude is defined as the eastern boundary of 3×10−6 s−1 at 850 hPa over 0°–\n20°N85.”\nReferences:\nWu, L., Wang, C. & Wang, B. Westward shift of western North Pacific tropical\ncyclogenesis. Geophys. Res. Lett. 42, 1537-1542 (2015).\nZhang, C. et al. Perspective on landfalling frequency and genesis location variations of\n45 / 63\nsouthern China typhoon during peak summer. Geophys. Res. Lett. 46, 6830–6838\n(2019).\nFig. R3.7 (i.e., Supplementary Fig. 13). Relationship of zonal shift in TUTT and\nMT with SST during July–September. a Time series of TUTT (blue) and MT (red)\nlongitude during 1979–2023. b Correlation of SST onto TUTT index. Hatching\nindicates correlation pass the 95% confidence level. Red (blue) contour is the 0.6 (0.3)\ncorrelation of SST onto the July–September PMM index (April–June NAT index).\nCorrelation coefficients between TUTT and PMM/NAT are marked. c Same as b but\nfor MT index.\n46 / 63\n7. Regression-training sample size and robustness: Training the TNAO→TTD\nregression on 1979–2014 (36 yr) risks sampling uncertainty; the validation period\n(2016–2023) showing higher R² may reflect lucky sampling or non-stationarity. The\nauthors can conduct sensitivity tests (e.g., leave-one-decade-out, bootstrap/jackknife)\nto quantify confidence intervals and show windowed or running regressions over\n1979–2023 to assess temporal stability of the linkage.\nResponse: Done as suggestion. We conducted bootstrap test and running\nregressions as shown in Fig. R3.8. Bootstrap test confirms the significant role of\nTNAO in modulating TTD, while running regression confirms the stability of\nTNAO-TTD relationship. We revised the manuscript as follows:\nLines 263–270: “To further validate the stability of the TNAO-TTD relationship\nand the prominent importance of TNAO relative to other factors, we employed the\nbootstrap method to assess the error range of regression coefficients and used the\nsliding regression to test the temporal stability of coefficients (Supplementary Fig. 17).\nThis prediction is generally independent of ENSO (Supplementary Fig. 18). Results\nindicate that only the TNAO coefficient in the regression model is statistically\nsignificant. Although the relationship between TNAO and TTD exhibits some\nfluctuation, it remains statistically significant throughout the study period.”\nAnd the method is as follows:\nSupplementary Methods: “To estimate the uncertainty of the regression\ncoefficients and correlation for multiple linear regression, a nonparametric bootstrap\nresampling method86 was applied to the data during 1979–2023 (all years included).\nFor each of 5000 bootstrap iterations, annual samples (corresponding to 45 years)\nwere randomly resampled with replacement from the original dataset to form a\npseudo-sample of equal size. Data are allowed to be selected repeatedly. Multiple\nlinear regression was then performed on each resampled dataset to obtain a new set of\nregression coefficients and the corresponding correlation between the predicted and\nobserved values. The distribution of these 5000 estimates was used to compute the 95%\nconfidence intervals, defined by the 2.5th and 97.5th percentiles.”\n47 / 63\nReference:\nAustin, P. C. & Tu, J. V. Bootstrap methods for developing predictive models. The\nAmerican Statistician 58, 131-137 (2004).\nFig. R3.8 (i.e., Supplementary Fig. 17). Stability of TNAO-TTD relationship. a\nBootstrap estimation (Supplementary Methods) of regression coefficients and\ncorrelation coefficient for predicting TTD using five climate indices (TNAO, NAO,\nNAM, NPO, SIC) and based on data during all years from 1979 to 2023. Blue bars\nindicate the mean regression coefficients, with black error bars representing the 95%\nconfidence interval. The red bar shows the mean correlation between observed and\npredicted TTD with its 95% confidence interval. b Sliding regression between TNAO\nand TTD over a 19-year moving window. The blue line with markers shows the\nregression in each window, and the shading indicates the 95% confidence interval.\n8. Mechanistic quantification: The proposed pathways (thermodynamic PMM vs.\ndirect circulation steering) are described qualitatively. Could the authors provide a\nquantitative analysis to show the relative importance of thermodynamic and dynamic\nfactors in the GPI analysis and quantify the consistent steering change? The authors\nmay also include precipitation/OLR composites to verify convective amplification\n48 / 63\nunder the PMM.\nResponse: Done as suggestion. For the GPI quantification, we follow the\nfollowing methods:\nLines 445–452: “To attribute GPI anomalies associated with the TNAO to\nindividual environmental factors, a linear decomposition was applied78. For each\nenvironmental factor, the corresponding TNAO-related anomaly was substituted into\nthe GPI calculation, while all other variables were retained at their climatological\nstates. The total GPI anomaly was subsequently approximated as the sum of the\ncontributions from all factors. This approach enables the relative roles of vertical\nwind shear (VWS), low-level relative vorticity (Vor), mid-level vertical velocity\n(Omega), mid-level relative humidity (RH), and potential intensity (Vpot) in\nmodulating TC genesis potential to be quantitatively assessed.”\nAnd a detailed analysis for each variable is given, as well as the quantification for\nsteering flow changes (Figs. R3.9-R3.10):\nLines 221–233: “To show the relative importance of thermodynamic and dynamic\nfactors in the GPI analysis, a linear decomposition is applied (see Methods). The\ncontributions of each variable are calculated, then the regional-mean is obtained for\neastern and western regions (outlined in Fig. 3b) associated with positive and negative\nGPI anomalies, respectively (Supplementary Fig. 13). The increased GPI in the\neastern WNP is mainly contributed by increase mid-level relative humidity, followed\nby increased lower-level vorticity, ascending motion and increased potential intensity\n(Fig. 3d). While the vertical wind shear does not significantly contribute to GPI\nchanges. In contrast, the decreased GPI in the western WNP is dominantly contributed\nby increased vertical wind shear (Fig. 3e). We also quantified the zonal steering flow\nchanges (Fig. 3f), given that the meridional component of steering flow change is\nnegligible. Both northern and southern regions (outlined in Fig. 3c) show significant\nchange, suggesting important guiding effects for typhoon movement.”\n49 / 63\nFig. R3.9 (i.e., Supplementary Fig. 14). Diagnosis of the GPI associated with the\nspringtime TNAO. a–f Regression of peak season GPI a and its individual\ncontributing terms during 1979–2023: b vertical wind shear (VWS), c absolute\nvorticity (Vor), vertical velocity d (Omega), e relative humidity (RH), and f maximum\npotential intensity (Vpot) onto the TNAO index during spring. Black boxes denote the\neastern (137.5°–170°E, 10°–27.5°N) and western (110°–135°E, 7.5°–17.5°N) analysis\nregions.\n50 / 63\nFig. R3.10 (i.e., Fig. 3) TNAO modulations on typhoon genesis and track.\nTNAO modulations on typhoon genesis and track. a Shading in the Pacific (Atlantic) is the\ncorrelation pattern of July–September SST onto the simultaneous PMM index (April–\nJune NAT index) during 1979–2023. Streamlines are the regressed 850-hPa horizontal\nwind onto the PMM+NAT index (WNP region). Three atmospheric systems are\nshown: monsoon trough (MT; represented by the relative vorticity of 3×10−6 s−1 at\n850 hPa), tropical upper-troposphere trough (TUTT; represented by the 12420-gpm\ngeopotential height at 200 hPa) and South Asian high (SAH; represented by the\n12500-gpm geopotential height at 200 hPa). Solid (dashed) contours are calculated as\nthe climatology plus (minus) the double regressions onto the PMM+NAT index.\n51 / 63\nGreen contours are the correlation of OLR onto PMM+NAT index, with interval same\nas the color bar (solid for positive and dashed for negative). b Regressions of peak\nseason Genesis Potential Index (shading) and typhoon genesis density (contour; 10−1\nyr−1) on the spring TNAO index. c Regressions of peak season 700-hPa geopotential\nheight (shading) and steering flow (vector; p < 0.05) onto the spring TNAO index.\nHatching indicates that it passes the 95% confidence level. d–e Regional mean GPI\ndiagnosis in the d Box 1 and e Box 2 (outlined in b). Bars show the GPI anomalies\nand contributions from each term, with error bars indicating the 95% confidence\nintervals. “Sum” denotes the sum of VWS term, Vor term, Omega term, RH term and\nVpot term. f Zonal steering flow anomalies regressed onto TNAO in Box 3 and Box 4\nin (outlined in c).\n9. Operational predictability: To underscore novelty, showing how a simple TNAO‐\nbased statistical forecast compares against state‐of‐the‐art dynamical seasonal\ntyphoon outlooks (e.g., ECMWF, GFS) in hindcast mode would demonstrate added\nvalue of the finding. Does the regression model outperforms the existing operational\nmodels?\nResponse: We thank the reviewer for this insightful suggestion. We fully agree\nthat comparing the TNAO-based statistical forecast with state-of-the-art dynamical\nseasonal typhoon outlooks (e.g., ECMWF SEAS5, CFSv2) would be valuable in\nfurther highlighting the novelty and potential added value of our approach. However,\nat present such a comparison is not feasible, because the operational seasonal\nforecasting centers only provide limited tropical cyclone products in graphical form\n(Fig. R3.11), without downloadable data fields. In particular, the ECMWF SEAS5\n“tropical storm density anomaly” is only available as charts. While the CFSv2 system\nprovides general atmospheric and oceanic variables but does not directly provide\ntropical cyclone density or track products. A rigorous comparison would thus require\nre-processing the raw seasonal forecast fields using dedicated cyclone tracking\nalgorithms, which is beyond the scope of this study. We further note that the TTD\n52 / 63\nmode is not a metric similar to tropical cyclone genesis or landfall frequency, but a\nleading mode that needs to be identified from track density. Due to the current\ndifficulties in data acquisition and calculation, we regret that we cannot compare the\nregression model with the existing operational models.\nNevertheless, we emphasize that the main objective of our work is to\ndemonstrate the physical role of the newly identified TNAO in modulating typhoon\nactivity, and to illustrate its potential as a simple statistical predictor compared with\nother major climate modes such as ENSO, AMO, PDO, NPO and NAO. We added\ndiscussions to highlight the possible value of TNAO research:\nLines 295–299: “Above results demonstrate that the TNAO provides physically\ngrounded and statistically independent information for predicting typhoon track\nvariability, enriching and deepening our understanding in East Asian typhoon activity\nbesides other major climate modes. Future work may focus on TNAO influence in\noperational dynamical forecast models to compare the ability of TNAO in forecasting\ntyphoon track pattern.”\nBesides, we have further added discussion on Relationship between TNAO and\nWNP cyclone in CMIP6 models (Fig. R3.13, i.e., Supplementary Fig. 20). Please see\nthe Response to the following Comment #11 for details.\n53 / 63\nFig. R3.11. Tropical cyclone forecast data in ECMWF. a Description of tropical\ncyclone trajectory data in ECMWF forecast data. Screenshot from\nhttps://www.ecmwf.int/en/forecasts/datasets/open-data . b Tropical storm standardised\ndensity production in SEAS5. Screenshot from\nhttps://charts.ecmwf.int/products/seasonal_system5_tstorm_density_anomaly .\nScreenshot was obtained by Oct 1, 2025.\n10. Physical process diagrams or schematic: Finally, a concise schematic that lays out\nboth the direct anticyclone‐driven trade weakening and the Atlantic‐tripole/Gill‐mode\nresponse pathway would help readers keep track of the multiple legs of the\ncross-basin teleconnection.\nResponse: Done as suggestion. Here we provided a schematic diagram (Fig.\nR3.12) at the end of the summary paragraph, and the detailed description is given in\nits caption:\nLine 370: “A schematic diagram is presented in Fig. 6.”\nFig. R3.12 (i.e., Fig. 6). Schematic diagram illustrating the influence of TNAO on\nWNP typhoons. During spring, the TNAO weakens trade winds over the subtropical\nnortheastern Pacific, leading to local SST warming and subsequent development of\nthe PMM through the seasonal footprint mechanism. Concurrently, TNAO strengthens\n54 / 63\nthe trade winds over the TNA, resulting in SST cooling that persists into summer via\nlocal air-sea coupling. The cold TNA SST anomalies can continuously induce Rossby\nwave anticyclone to its west, thereby further promoting PMM development. The\nAtlantic-eastern Pacific Rossby wave anticyclone has been demonstrated to directly\nfavor the WNP cyclone via Pacific convection, a phenomenon that is explained by the\nPASO. Consequently, the presence of a lower-level cyclone and an upper-level\nanticyclone in the WNP leads to an eastward shift in the TUTT and MT (solid line for\npositive TNAO and dashed line for negative TNAO), promoting more (less) frequent\ntyphoon genesis in the eastern (western) WNP. The altered steering flow favors\n(inhibits) typhoon movement towards higher (lower) latitudes of East Asia.\n11. Translation from projected TNAO to northward shift of typhoon activity: The\nauthors show an increased frequency of positive TNAO events under SSP5-8.5, but\nhow that translates into societal risk (e.g. changes in ACE, landfall probability,\neconomic exposure) remains unexplored. A brief discussion would help readers grasp\nthe real-world implications. It will be much convincing if the authors can demonstrate\nthe connection by analyzing typhoon activity in CMIP6 models ensemble simulations.\nResponse: We thank reviewer’s insightful comments. We added some\ndiscussions as follows to show how projected TNAO change may translate into\nsocietal risk. We acknowledge that it will be much convincing if we demonstrate the\ntyphoon activity in CMIP6 models ensemble simulations. However, the CMIP6\noutputs we used (see Methods) are monthly data and have a coarse resolution, with\nmost grid cells spaced approximately 200-km apart. Therefore, we regret that we are\nunable to carry out this work at present.\nNevertheless, we investigated the future changes in steering flows over the WNP\nin response to TNAO. As shown in Fig. R3.13, climate models can well capture the\nstrengthening cross-seasonal linkages between the WNP anomalous cyclone and\npreceding spring TNAO with an intensification as the climate warms. Moreover, the\nTNAO mode obtained from the CMIP6-MME well mirror the observed one (Fig. 5b\n55 / 63\nvs Fig. 1b; with a robust pattern correlation, R = 0.97). This result indirectly lends\nmore confidence to our hypothesis. We added related discussion as follows:\nLines 306–325: “As shown in Fig. 5a, the CMIP6 models generally (23 out of 26\nmodels) project an increase (~39.4%) in positive TNAO events during the 21st\ncentury (23.96 ± 3.28 events per 100 years), compared to that during the 20th century\n(17.19 ± 1.85 events per 100 years). Moreover, the TNAO mode obtained from the\nCMIP6-MME well mirror the observed one (Fig. 5b vs Fig. 1b; with a robust pattern\ncorrelation, R = 0.97).\nThe increase in positive TNAO event is most pronounced toward the end of the\n21st century, exceeding 20 events per 100 years (Fig. 5c). Moreover, there are 7 out of\nthe WNP anomalous cyclone and preceding spring TNAO (Supplementary Fig. 20),\nfurther implying more prominent impacts on typhoon track potentially from TNAO as\nthe climate warms. In addition, the future anthropogenic warming would decelerate\ntyphoon motions near high-latitude populated coastal regions due to a poleward shift\nof the subtropical westerlies, which can induce a longer time of typhoon impacts57.\nAnd accompanying with increasing intensity and rainfall58, this can potentially\ncompounds future typhoon-related damages. Since based on our current results, if the\nincrease in positive events of TNAO can cause the northward shift of typhoon track,\nhigh-latitude cities in East Asia would face more typhoon frequency, greater ACE and\nincreased economic losses in future. Therefore, we have every reason to infer that the\npoleward migration of typhoon activity induced by TNAO in a warming future would\nexacerbate the societal risk from typhoons in East Asian high-latitude cities as the\ncentury progresses.”\n56 / 63\nFig. R3.13 (i.e., Supplementary Fig. 20). Relationship between TNAO and WNP\ncyclone in CMIP6 models. a Correlation coefficient between spring TNAO and peak\nseason cyclonic steering flow in the WNP during 1900–1999 (blue bars) and 2000–\n2099 (red bars). The selected 7 ensembles are in the shaded background with error bar\nshowing the 95% confidence interval for the selected MME. The steering flow index\nis defined as zonal steering flow difference between (12.5°N–22.5°N, 130°E–170°E)\nand (27.5°N–37.5°N, 115°E–155°E). b Mean of regression patterns of peak season\nsteering flow (vector) onto the spring TNAO index during 1900–1999 for the selected\n7 ensembles. Shading is the correlation of zonal component of steering flow onto the\nTNAO index. c Same as b but during 2000–2099.\nMinor Comments\n1. The global analyses (EOFs, regressions, composites) lack a clear statement of their\nfull temporal coverage (e.g., 1979–2023) beyond the training/validation split.\nTherefore, please explicitly state the data period in the Methods and all Fig. captions\n57 / 63\n(e.g., “analyses based on 1979–2023”).\nResponse: Done as suggestion. Data period was stated the Methods and all\nFig./Table captions, including:\n“Fig. 1. Spatial pattern of TNAO and its dynamical features. a Correlation\nbetween zonally-averaged SLP anomalies in 25°W–105°W during 1979–2023.”\n“Fig. 2. Impacts of spring TNAO on summer Asia–Pacific typhoon activity. a\nScatter diagram of the spring TNAO (red) and extratropical NAO (blue) against the\nTTD of peak season typhoon track density during 1979–2023.”\n“Fig. 3. TNAO modulations on typhoon genesis and track. a Shading in the\nPacific (Atlantic) is the correlation pattern of July–September SST onto the\nsimultaneous PMM index (April–June NAT index) during 1979–2023.”\nMethods: “We focus on the East Asia–WNP typhoon (TC with V > 32.6 m s−1)\nmax\nduring July–September of 1979–2023, using the Joint Typhoon Warning Center TC\nbest-track datasets.”\n2. “high summer” vs. “peak season”: The manuscript alternates between “high\nsummer” (July–August) and “peak season” (July–September), which can confuse\nreaders. Please keep “peak season” since all analyses seemed to have focused on peak\nseason.\nResponse: Done as suggestion. We have replaced all “high summer” with “peak\nseason”.\n3. Please give the rationale for excluding extreme El Niño years from the analysis and\nbriefly discuss and explicitly show its potential impact.\nResponse: Examining the potential influence of ENSO is important. Therefore,\nwe provided scatter plots between Niño3.4, TNAO, and TTD under different\nconditions (Fig. R3.13). During 1979–2023, Niño3.4 and TNAO show a weak\ncorrelation (R = 0.28, p = 0.07; Fig. R3.13a). After removing three strong El Niño\n58 / 63\nyears, the correlation becomes insignificant (R = 0.18, p = 0.25; Fig. R3.13b). When\nwe instead remove the three strongest La Niña years (1988, 1999, and 2007), the\ncorrelation also becomes insignificant (Fig. R3.13c), and the TNAO–TTD\nrelationship (R = 0.73; Fig. R3.13d) slightly weakens compared to that in the main\ntext (R = 0.75). Furthermore, excluding additional ENSO years yields a similar result\n(R = 0.72; Fig. R3.13e), again slightly lower than 0.75. These results confirm that our\nmain conclusion is robust, ensuring that the TNAO–TTD correlation remains as high\nas possible while maintaining an insignificant linear relationship between TNAO and\nNiño3.4.\nLines 149–153: “Given that relationship between TNAO and ENSO is not\nsignificant (even in a longer period of 1958–2023; Supplementary Figs. 6a–e), we\nhave tested and found that even in the case of excluding all ENSO years, the\nTNAO-TTD relationship still keeps robust (R = 0.72, p < 0.01; Supplementary Figs.\n6f–g). Accordingly, the cross-seasonal TNAO-TTD linkages are generally\nindependent of ENSO signals.”\n59 / 63\nFig. R3.14 (i.e., Supplementary Fig. 6). Relationship between ENSO and TNAO.\na Scatter plot of spring TNAO versus preceding winter Niño3.4 index during 1958–\n2023. b Same as a but for peak season Niño3.4 index. c Same as b but during 1979–\n2023. d Same as c but excluding years 1982, 1997 and 2015. e Same as d but further\nexcluding extreme La Niña years of 1988, 1999 and 2010 (selected by three lowest\nNiño3.4 index years). f Same as e but for TNAO versus TTD. g Scatter plot of spring\nTNAO versus TTD excluding El Niño and La Niña years that Niño3.4 index\nexceeding 1.0 absolute value. Correlation coefficients and their p-values are marked.\n4. L146–151: Clarify that the anticyclonic flow over the northeast Pacific produces\nwesterly anomalies on its equatorward flank, vectorially opposing the northeasterly\ntrades since this could not be seen from Supplementary Fig. 5a.\n60 / 63\nResponse: As shown in Fig. R3.4 (i.e., Supplementary Fig. 11), the TNAO-related\nanticyclone can produce southwesterly anomalies over the subtropical northeastern\nPacific, therefore weakening trade winds (Fig. R3.5, i.e., Supplementary Fig. 9) and\ncontributing to the development of PMM. Please check our response to major\ncomment 4 for details.\n5. Lines 50–51 “While the impacts of shifting internal natural climatic oscillations on\ntyphoon activity are often overlooked” is not true. There have been many studies that\ninvestigated the impacts of various internal long-term climate modes on typhoon\nactivity, including PDO, IPO, AMO, etc. The authors should mention those here.\nResponse: Of note is that here we use a “shifting” to emphasize the shifting\natmospheric oscillations. So our intention is to suggest that research of atmospheric\noscillations impacts on typhoon is relatively less than other researches, shifting ENSO\nfor example, on typhoon changes. Here we also added some studies on the impacts of\nvarious internal long-term climate modes (e.g., PDO, IPO, AMO) on typhoon activity.\nThe manuscript is revised as follows:\nLines 49–53: “Impacts of North Pacific and Atlantic climate shifts on typhoon\nactivity are also highlighted. For example, interdecadal changes in Northwest Pacific\n(WNP) tropical cyclone activity can be attributed to the Atlantic Multidecadal\nOscillation (AMO), Interdecadal Pacific Oscillation (IPO) and/or Pacific Decadal\nOscillation (PDO)14-20. While the impacts of shifting internal atmospheric oscillations\non changes in typhoon activity are often overlooked.”\nReferences (14-20):\nCao, J., Feng, J., Zhao, H., Wang, B., Wu, L., & Wang, C. (2025). Comparable\nimpacts of IPO and AMO on the decadal change of northern hemisphere tropical\ncyclone frequency. Clim. Dyn., 63(1), 23.\nhttps://doi.org/10.1175/JCLI-D-24-0731.1\nChan, J. C., & Liu, K. S. (2022). Recent decrease in the difference in tropical cyclone\n61 / 63\noccurrence between the Atlantic and the western North Pacific. Adv. Atmos. Sci.,\n39(9), 1387-1397. https://doi.org/10.1007/s00376-022-1309-x\nHan, Y., Jiang, W., Jiang, L., Yong, Y., Yang, K., & Gu, T., et al (2024). Corals reveal\ninterdecadal variation of tropical cyclones modulated by Pacific Decadal\nOscillation. J. Geophys. Res.-Atmos., 129(7), e2024JD040824.\nhttps://doi.org/10.1029/2024JD040824\nWang, C., Wang, B., Wu, L., & Luo, J. J. (2022). A seesaw variability in tropical\ncyclone genesis between the western North Pacific and the North Atlantic shaped\nby Atlantic multidecadal variability. Journal of Climate, 35(8), 2479-2489.\nhttps://doi.org/10.1175/JCLI-D-21-0529.1\nZhao, J., Zhan, R., Kim, D., Kug, J. S., Long, J., & Zhang, L., et al (2024). Distinct\nmodulations of Northwest Pacific tropical cyclone precipitation by Atlantic\nMultidecadal Oscillation and Interdecadal Pacific Oscillation. Geophys. Res.\nLett., 51(12), e2023GL107749. https://doi.org/10.1029/2023GL107749\nZhao, J., Zhan, R., Wang, Y., Xie, S., & Wu, Q. (2020). Untangling impacts of global\nwarming and Interdecadal Pacific Oscillation on long-term variability of North\nPacific tropical cyclone track density. Sci. Adv., 6(41), eaba6813.\nhttps://doi.org/10.1126/sciadv.aba6813\nZhao, J., Zhan, R., Wang, Y., & Xu, H. (2018). Contribution of the interdecadal\nPacific oscillation to the recent abrupt decrease in tropical cyclone genesis\nfrequency over the western North Pacific since 1998. J. Clim., 31(20),\n8211-8224. https://doi.org/10.1175/JCLI-D-18-0202.1\n6. Although references are comprehensive, some recent work on cross-basin\nteleconnections where relevant should be included.\nResponse: We agree that there should be references on cross-basin\nteleconnections since we discuss a lot of relevant mechanisms. Therefore, the\n62 / 63\nfollowing references are included:\nReferences:\nCapotondi, A., Mcgregor, S., Mcphaden, M. J., Cravatte, S., Holbrook, N. J., & Imada, Y., et al\n(2023). Mechanisms of tropical Pacific decadal variability. Nat. Rev. Earth Environ., 4(11),\n754-769. https://doi.org/10.1038/s43017-023-00486-x\nFu, M., Wang, C., Wang, B., Wu, L., Cao, J., & Zhao, H. (2025). Pacific Meridional Mode as a\nBridge in Linking North Atlantic Oscillation and Interdecadal Variability in Tropical Cyclone\nGenesis in the Western North Pacific. J. Clim., 38(19), 5431-5442.\nhttps://doi.org/10.1175/JCLI-D-24-0731.1\nKao, P., Hong, C., Huang, A., & Chang, C. (2022). Intensification of interannual cross-basin SST\ninteraction between the North Atlantic tripole and Pacific meridional mode since the 1990s. J.\nClim., 35(18), 5967-5979. https://doi.org/10.1175/JCLI-D-21-0594.1\nLi, S., Xiao, Z., & Zhao, Y. (2023). Modulation of the influence of ENSO on northward-moving\ntropical cyclones in the Western North Pacific by the North Atlantic tripole SST anomaly\npattern. J. Clim., 36(2), 405-420. https://doi.org/10.1175/JCLI-D-21-0704.1\nPark, J., Yeh, S., Kug, J., Yang, Y., Jo, H., & Kim, H., et al (2023). Two regimes of inter-basin\ninteractions between the Atlantic and Pacific Oceans on interannual timescales. npj Clim.\nAtmos. Sci., 6(1), 13. https://doi.org/10.1038/s41612-023-00332-3\nTian, Q., Yu, J., Nnamchi, H. C., Li, T., Li, J., & Li, X., et al (2025). Unraveling the mystery of\nrecent shortened response time of ENSO to Atlantic forcing. Nat. Commun., 16(1), 5884.\nhttps://doi.org/10.1038/s41467-025-61130-4\nZhu, Y., Xu, S., Huang, F., & Ding, Z. (2025). Modulation of landfalling tropical cyclone activity\nin the western North Pacific by North Atlantic tripolar SST. Atmos. Res., 322, 108130.\nhttps://doi.org/10.1016/j.atmosres.2025.108130\nZhang, L., Yang, X. & Zhao, J. Impact of the spring North Atlantic oscillation on the Northern\nHemisphere tropical cyclone genesis frequency. Front. Earth Sci. 10, 829791 (2022).\nZhao, J., Zhan, R., Kim, D., Kug, J. S., Long, J., & Zhang, L., et al (2024). Distinct modulations\nof Northwest Pacific tropical cyclone precipitation by Atlantic Multidecadal Oscillation and\nInterdecadal Pacific Oscillation. Geophys. Res. Lett., 51(12), e2023GL107749.\nhttps://doi.org/10.1029/2023GL107749\n6. Line 58, remove “attribution of”.\nResponse: Corrected.\n8. Line 537, change “exerts” to “exert”.\nResponse: Corrected.\n63 / 63\nResponse to Reviewers’ comments\nREVIEWERS' COMMENTS\nReviewer #1 (Remarks to the Author):\nI believe that the authors have addressed all my concerns. I am satisfied with current revision\nand suggest to accept this manuscript.\nReviewer #2 (Remarks to the Author):\nIt seems that all my concerns have been addressed in the revised MS and responses to\nreviewers. The reviewer felt that the MS could be accepted for publication.\nReviewer #2 (Remarks on code availability):\nThese codes are relatively complete and can be easily read and reproduced.\nReviewer #3 (Remarks to the Author):\nSummary and Overall Assessment\nThe revised manuscript presents the identification of a previously unrecognized\nclimate mode—the tropical-leaning North Atlantic Oscillation (TNAO)—and argues\nthat this springtime SLP dipole is a strong and physically meaningful predictor of\npeak-season western North Pacific (WNP) typhoon track variability, particularly the\npoleward migration toward high-latitude East Asian cities. The authors propose two\nphysical pathways linking TNAO to the Pacific Meridional Mode (PMM), tropical\nNorth Atlantic SST anomalies, and subsequent summertime steering flow anomalies.\nThey also examine CMIP6 projections, suggesting more frequent positive TNAO\nevents under SSP5–8.5.\nThis work proposes a novel and interesting cross-basin teleconnection with\npotential implications for seasonal typhoon forecasting. The revisions substantially\nimprove the clarity, robustness, and physical justification of the results. The authors\nhave responded carefully and extensively to reviewer comments, adding new analyses\n(trend-removed fields, ENSO controls, GPI decomposition, jet shift index revision,\nadditional figures, sensitivity tests) and clarifying mechanisms.\nOverall, the revised manuscript is strong and now suitable for publication\nafter addressing a few remaining issues that concern clarity, over-interpretation,\nand the need for more cautious framing in several places as summarized in my\ncomments given below.\nResponse: We thank the reviewer for the positive and constructive comments.\nWe have addressed the remaining concerns regarding clarity, cautious interpretation,\nand framing throughout the manuscript. Please check our point-to-point responses\nbelow.\nRemaining Issues to Address Before Acceptance\n1. Over-extrapolation of CMIP6 TNAO projections to future typhoon risks: While the\nauthors acknowledge limitations, some statements still imply direct future increases in\ntyphoon hazards due to TNAO changes (e.g., Lines 324–329). CMIP6 outputs used\nhere do not simulate typhoons directly, and steering flow differences alone cannot\nfully translate to future track density changes because the latter are also strongly\naffected by shifts in genesis locations. Furthermore, the response of TC activity over\nthe WNP to the projected changes in TNAO might be also sensitive to the global\nwarming-induced SST patterns in the Tropics (see a review by Wang et al. 2025),\nwhich has not been examined in this study and thus be mentioned in the discussion\nsection. I thus suggest further soften any direct implication that increased positive\nTNAO would cause increased typhoon frequency or ACE near high-latitude cities and\nemphasize instead where applicable (including the abstract)“…may potentially\ncontribute to…” “suggests possible increases in steering-flow patterns conducive to\npoleward migration…”.\nWang, Y., M. Satoh, R.-F. Zhan, J.-W. Zhao, and S.-P. Xie, 2025: Tropical sea surface\nwarming patterns and tropical cyclone activity – A review, Adv. Atmos. Sci., 42(10),\n1996−2017, https://doi.org/10.1007/s00376-025-5114-1.\nResponse: We thank the reviewer for pointing out this important comment. We\nfully agree that in our study, CMIP6 models do not explicitly simulate typhoon\ndirectly and that steering-flow anomalies alone cannot be directly translated into\nfuture changes in typhoon track density. We also agree that the WNP typhoon\nresponse to projected TNAO changes may depend on global-warming-induced\ntropical SST patterns (Wang et al. 2025), which has been mentioned in the revised\ndiscussion. Per the reviewer’s suggestion, we have softened all statements that\npreviously implied direct causal increases in future typhoon hazards. Specifically:\nLines 38–40: “This may potentially contribute to a poleward migration of\ntyphoon activity/threats toward East Asian high-latitude cities as climate warms, yet\nuncertainty remains due to model biases in simulating tropical sea surface temperature\npatterns.”\nLines 332–336: “Based on our current results, the projected increase in positive\nevents of TNAO may potentially contribute to increases in steering-flow patterns\nconducive to poleward migration of typhoon tracks. Such circulation changes imply\nthe possibility that high-latitude cities in East Asia would face more typhoon threats\nand relevant economic losses in future.”\nLines 344–351: “Besides, CMIP6 outputs used here do not simulate typhoon\ntracks directly, and steering-flow changes alone cannot fully translate to future track\ndensity changes because the latter are also strongly affected by shifts in TC genesis\nlocation. Furthermore, considering that the influence of TNAO on WNP circulations\ndepends on teleconnection processes between the ocean and atmosphere, the response\nof typhoon activity over the WNP to the projected changes in TNAO might be\nsensitive to the global warming-induced SST patterns in the tropics63, thereby altering\nthe TNAO-TTD relationship, also causing uncertainty.”\n2. Clarify the added “city-box” frequency results: The added frequency increments\n(+1.0/–0.65) are useful but need more explanation, such as whether they are counts\nper season, or normalized by area, or if the two boxes are comparable in size, and the\nnumbers should be area-adjusted. I suggest add a sentence explaining the units and\ncomparability.\nResponse: We appreciate the reviewer’s suggestion. In the revised manuscript,\nwe explicitly explain that the reported values (+1.0/–0.65) represent the change in\nthe number of typhoons entering each box per peak season in response to a one–\nstandard deviation change in the TNAO index. We also clarify that the two boxes\ndiffer in size and therefore provide an area-normalized comparison. Specifically, we\nhave added the following sentences:\nLines 178–184: “The typhoon frequency is calculated by counting typhoon in\npeak season entering the boxes in Fig. 2b. A one standard deviation change in the\nTNAO index corresponds to an increase of 1.0 ± 0.45 yr−1 in typhoon frequency in\nnorthern East Asia (see red box) and a decrease of 0.65 ± 0.45 yr−1 in typhoon\nfrequency in southern East Asia (see blue box). When normalized by area, the\ntyphoon-frequency changes are similar in magnitude between the two regions, with\nabout 0.46 yr−1 and 0.41 yr−1 typhoons per million square kilometers in the southern\nand northern boxes, respectively.”\n3. Potential circularity between PMM definition and TNAO-forced Pacific SST: The\nPMM region used in the lead-lag analysis partly overlaps with the area of westerly\nanomalies produced by TNAO. While this is physically plausible, it can also appear\nself-reinforcing. I would recommend add 1–2 sentences acknowledging this potential\ndependence and explaining why the interpretation remains robust.\nResponse: Of note is that perhaps someone might think that the PMM region\nused in the lead-lag analysis (Supplementary Fig. 8) partly overlaps with the area of\nanticyclonic flows induced by TNAO (Supplementary Figs. 8a, 11a). However, this\noverlap does not imply a potential circularity between TNAO (or TNAO-forced\nPacific SST) and PMM, because the summertime PMM index exhibits a clear lagged\nresponse to springtime TNAO index (Supplementary Fig. 8b). In other words, the\nsummertime PMM is just as one of bridges linking TNAO signal to subsequent\ntyphoon activity. To address this concern, we have rewritten relevant statements as\nfollows:\nLines 188–193: “On the one hand, the springtime TNAO can directly promote the\nsubsequent Pacific Meridional Mode (PMM) SST anomalies through the\nTNAO-related anticyclonic flows (Supplementary Figs. 8a–b). Since this anticyclonic\nflows can weaken local trade winds in the subtropical Northeast Pacific (red rectangle\nin Supplementary Figs. 9 and 11a), and thereby the resultant warm SST (wind–\nevaporation–SST feedback mechanism) facilitates PMM’s development via the\nseasonal footprint mechanism27,35.”\n4. Strength of the jet-TNAO relationship: The NAJS–TNAO correlation (R ≈ 0.85) is\nvery high, implying the two indices may be nearly redundant. The manuscript should\nclarify whether TNAO provides additional information beyond NAJS for typhoon\nprediction and why TNAO is more directly connected to steering-flow anomalies than\nNAJS alone. A brief statement would resolve this.\nResponse: We appreciate the reviewer’s observation regarding the strong\ncorrelation between NAJS and TNAO. To clarify their distinction and relative roles,\nwe have added an explanatory sentence in the revised manuscript:\nLines 165–171: “Of note is that NAJS exhibits a weaker correlation with TTD (R\n= 0.64), albeit NAJS and TNAO are highly correlated (R = 0.85; Supplementary Fig.\n3c).. This may be due to that the NAJS index is defined from U200 anomalies within\na smaller region of the upper-tropospheric jet stream. In contrast, TNAO is a\nlower-tropospheric SLP mode that spans a broader tropical domain, the associated\nair-sea interactions are inevitably stronger than that for NAJS, leading to a closer\nrelationship from TNAO to TTD.”\nMinor Comments\n1. Lines 29–32, “covariance above 56%” — specify this refers to TNAO explaining\n56% of TTD variance.\nResponse: Revised as suggestion.\n2. Line 85, change to “…can directly capture the previously overlooked TNAO\nsignal.”\nResponse: Revised as suggestion. Line 85-86.\n3. “TUTT” appears as “tropical upper-tropospheric trough” in line 211 while “tropical\nupper-troposphere trough” in line 759. Use uniform terminology.\nResponse: Thanks. Done as suggestion."}]

Decision letter