A snow-fire bridge mechanism for the 2025 Southern California winter wildfire

Domain: biology
Published: 2026-03-19
Editorial outcome: minor_revision

Abstract
In January 2025, a rare and highly destructive wildfire devastated Southern California, becoming the costliest wildfire event in recorded history. The unusual timing of this wildfire suggests the possibility of unique remote, large-scale climatic precursors that differ from those of previous wildfires. Here, through observational analysis and large-ensemble numerical simulations, we identify that western Eurasian snow cover reduction is associated with weather conditions favorable for wildfires in Southern California in December-January, and simulations indicate that this can occur via an atmospheric teleconnection from western Eurasia, across the North Pacific and into North America. Both observations and simulations show that the reduced snow cover over western Eurasia contributes to the typical wintertime western warming-eastern cooling dipole pattern in North America. The main dynamical mechanisms involve downstream propagating Rossby wave trains triggered by the reduced snow cover in western Eurasia, as well as wave-mean flow interaction over the North Pacific. Our study suggests that Eurasian snow cover anomalies can be predictively linked to both subsequent wildfire risk in California and the wintertime North American zonal dipole temperature pattern, highlighting the broader impacts of Eurasian cryosphere variability on remote climate extremes.

Introduction
In recent years, extreme wildfires have become more frequent and severe, posing serious threats to natural ecosystems and human society globally1-4. Recent examples include the extreme wildfires in Canada in May 20235, the devastating fire in Hawaii in August 20236, the catastrophic wildfire events in Southern California in January 20257,8 and the largest wildfire in South Korea's recorded history in March 20259. Given this trend, there is an urgent need to better understand the climatic drivers behind these events and to raise broader awareness of their impacts.

California has long been a hotspot of wildfires, and global warming has further extended its fire season from summer to autumn or even winter8,10-12. In January 2025, a series of extreme wildfires in Southern California killed at least 29 people and caused over 250 billion dollars in damage7,8, making it one of the most destructive and costliest events ever recorded in California’s fire history. In fact, when a fire occurs in fall or winter in this region, it often causes more damage than those occurring in the summer active-fire season13 due to enhanced, anomalous atmospheric circulations with stronger winds promoting rapid fire spread14,15 and typically limited resources like firefighting capacity11. Understanding the drivers of winter wildfires in the context of recent climate change is an essential first step toward raising public and governmental risk awareness about these emerging extremes.

Over the past two decades, many studies have examined the climatic drivers influencing the frequency, duration, intensity, long-term trends and interannual variations of summer-autumn wildfires in California. These drivers have been attributed to various local and large-scale factors, including early spring snowmelt16, reduced precipitation and humidity17-19, Santa Ana winds15,18,20-22, anthropogenic warming10,12,23-26, and large-scale oceanic and atmospheric variability24,27-30. Winter wildfires have rarely occurred in California, where the winter condition is climatologically cold and wet, and thus have received comparatively little attention in scientific research. The extreme wildfires in Southern California in January 2025 and the Thomas Fire in December 2017 challenged this traditional paradigm, pointing to a potentially rising winter fire risk under anthropogenic global warming. Although local meteorological conditions have been shown to contribute to the unusual winter wildfire event this year8, the potential large-scale climatic drivers behind this event remain poorly understood.

In this study, we aim to investigate the large-scale climatic drivers and the underlying mechanisms of Southern California winter wildfires with implications for the recent 2025 extreme event. In contrast to previous studies that primarily focused on anthropogenic warming and large-scale oceanic and atmospheric variability affecting summer and autumn California wildfires10,23,24,30, our study has identified a unique but overlooked mechanism for western Eurasian snow cover to influence winter wildfires in Southern California through an atmospheric teleconnection mechanism. Previous observational and modeling studies have shown that extensive and persistent Eurasian snow cover anomalies can induce remote influences31-35, for example, on the wintertime temperature in eastern North America and western Europe through Rossby wave propagation and the troposphere–stratosphere coupling mechanisms32,34. Similarly, other modeling studies have demonstrated that increased autumn snow cover over the Tibetan Plateau can induce Pacific-North America (PNA) or West Pacific (WP)-like atmospheric circulations through eastward propagating Rossby waves and the North Pacific transient eddy feedback, which further influence the North American climate33,36,37. Here, extending this view to wildfire extremes, we combine observational analysis and targeted climate model experiments to demonstrate that a preceding reduction of western Eurasian snow cover has potentially contributed to the recent Southern California wildfires in January 2025 through atmospheric teleconnections.

Results
Observational hints of the snow-fire teleconnection
Our investigations focus particularly on the December–January (DJ) fire weather index (FWI, see Data and Methods) and vapor pressure deficit (VPD, see Data and Methods) . The choice of DJ average, rather than January alone, is motivated by the facts that this particular 2025 wildfire event occurred in early January and that the occurrence of wildfires can be influenced by preceding meteorological conditions5,38. Since few wildfires have historically occurred during this season, with the Thomas Fire in December 2017 being the most well-known example, our study uses FWI rather than burned area as the primary metric for establishing the statistical linkage. Previous studies have demonstrated that the FWI corresponds well with observed wildfire events, including remotely sensed fire detections, supporting its use as a proxy for wildfire activity in regions or periods with limited burned area records23,39. VPD is another useful metric capturing wildfire potential40 and is strongly correlated with the FWI in Southern California (r = 0.92; Supplementary Fig. 1a). The years witnessing more burned areas correspond closely to the years with extreme FWI and VPD values (Supplementary Fig. 1b-c), although a high FWI or VPD does not necessarily always imply a high wildfire activity that is also influenced by, for example, fuel availability, ignition and fire suppression efforts23,41.

Wildfires in Southern California like the January 2025 one (Fig. 1c) are typically favored by a local high-pressure system that raises temperature, suppresses precipitation and humidity, and causes Santa Ana winds to penetrate from the inland mountains toward the coast8,21,38. These local weather conditions conducive to wildfires are often thought to be partly driven by large-scale climatic factors especially over oceans29,30,39. Among them, La Niña is considered as one of the leading contributors39,40 and was speculated to have contributed to this unusual wildfire event45. However, the La Niña condition in 2024/2025 was rather weak46. Our analysis suggests that the anomalously high FWI in December 2024-January 2025 largely exceeded the level that the concurrent La Niña could potentially explain (Supplementary Fig. 2), suggesting the possibility of other large-scale climatic drivers.

We have systematically investigated the statistical relations between the DJ FWI in Southern California and potential climate drivers over global oceans and land. The most robust correlation identified is with the preceding November-December (ND) snow cover extent (SCE) anomalies in western Eurasia (10°E-55°E & 48°N-60°N) on the other side of the globe (r=-0.59, p<0.01). Our lead-lag analysis suggests that their correlation maximizes when SCE leads DJ FWI by 1-2 months and drops substantially when SCE lags (Supplementary Fig. 3). Although the El Niño-Southern Oscillation (ENSO) is overall an important oceanic forcing for wintertime climate variability in North America, the correlation between the ND Niño3.4 and the DJ FWI in Southern California is relatively weak (r=-0.18). When the influence of ENSO is removed, the snow-fire correlation coefficient is further boosted to -0.64 (Fig. 1a), that is, an anomalously high DJ FWI in Southern California is typically preceded by a reduction in ND SCE in western Eurasia (Fig. 1b,c).

The snow-fire correlation is robust across multiple satellite-derived snow cover datasets and different observational FWI datasets during 2000-2025 (Supplementary Table 1). Detrending does not significantly influence the correlation here as interannual variability dominates over the trend during this period. A nonparametric bootstrap approach is then used to demonstrate that the snow-fire correlation is not dominated by a single extreme year (Supplementary Fig. 4). After constructing a statistical model trained on the western Eurasia ND SCE index over 2000–2024 and then applying it to the hindcast of the 2025 event, we find that it can explain approximately 68% of the VPD anomaly and 40% of the FWI anomaly in 2025 from a statistical perspective.

What is the atmospheric pattern that connects the ND SCE reduction in western Eurasia to the DJ wildfire events in Southern California? The western Eurasian SCE reduction is associated with a pronounced atmospheric wave train, illustrated by the DJ 200 hPa geopotential height and meridional wind fields, that extends from western Eurasia across the continent and the North Pacific to North America, leading to an anomalous anticyclonic circulation over the western U.S. and adjacent seas (Fig. 2a,b). The resultant high-pressure ridge is accompanied by a large-scale subsidence and a reduced cloud cover (Supplementary Fig. 5), which together act to increase surface maximum temperature in the western U.S. through dynamical and radiative warming (Fig. 2c). The strong subsidence also reduces precipitation and relative humidity in this region (Fig. 2d,f), which contributes to the decreased dead fuel moisture and enhanced potential fire intensity over Southern California during December–January (Supplementary Fig. 6). The combined effect of increased temperature and reduced humidity is to significantly enhance the VPD over the western U.S. (Fig. 2g). In addition, the strengthened northeasterly offshore winds near Southern California intensify the Santa Ana winds, which can more rapidly spread wildfires21,22,45. All these changes in meteorological conditions contribute to the substantial increase in DJ FWI across the western U.S., especially in Southern California. Such changes in VPD and FWI are already present in November but overall weaker (Supplementary Fig. 7), which can also contribute to subsequent wildfire events. Overall, observational regression analysis suggests that reduced snow cover over western Eurasia may significantly contribute to November-January wildfire weather in Southern California through large-scale atmospheric teleconnections.

The SCE reduction in western Eurasia is associated with not only favorable weather conditions for Southern California wildfires but also a continental-scale DJ temperature pattern with warming in western North America and cooling in eastern North America (Fig. 2c). This winter temperature zonal dipole pattern has drawn widespread attention in recent literature46-49 and has been shown to be related to droughts in the western U.S. and snowstorms in the eastern U.S.52. Previous studies have attributed this pattern to various drivers, including atmospheric circulation variability49,53 and sea surface temperature (SST) variations50. Our observational analysis suggests that western Eurasian SCE reduction may be another contributing factor to this winter temperature zonal dipole pattern (Fig. 2c; Supplementary Fig. 8) over North America via atmospheric teleconnections (Fig. 2a). This finding lends support to the argument that western Eurasian SCE changes can potentially serve as a useful predictor for winter climate variability across the U.S.54.

The next key question to explore is whether the snow-fire connection identified in our observational analysis reflects a causal link or merely a simultaneous response to certain atmospheric circulation patterns55. It has already been demonstrated in previous modeling studies that Eurasian snow cover changes have the potential to influence the downstream climate through eastward propagating Rossby wave trains along the mid-latitude jet stream33,56. Despite that, targeted numerical simulations are necessary to explicitly demonstrate the causality, if any, in the snow-fire connection identified here and to quantify the impact of SCE reduction in western Eurasia on the winter wildfires in Southern California.

Modeling evidence of the snow-fire teleconnection
To that end, we conduct large-ensemble coupled model experiments with modified SCE conditions in western Eurasia using the Community Earth System Model version 2.1 (CESM2.1)57 (Data and Methods). Firstly, ten different initial conditions are selected at five-year intervals from a long control simulation. Then, for each initial condition, two sets of 12-member experiments with slightly different atmospheric initial conditions (with a difference of approximately 10-14 K) are performed with a different prescribed SCE forcing in each set calculated from the NOAA SCE dataset. In the first set (named “ClimSCE”), the climatological SCE from October to January during 2000-2025 is prescribed in western Eurasia (10°E-55°E & 48°N-60°N); in the second set (named “LessSCE”), the true SCE during October 2024-January 2025 is used instead (Supplementary Fig. 9). Snow cover outside this region is computed by the model. All the simulations are integrated from October 1 through February 1 of the following year. Each set consists of 120 simulations in total, and the large-ensemble setup allows us to robustly isolate the signal from the influence of internal climate variability.

In late 2024, the SCE decreased by over 20% of the climatological amount in most of western Eurasia especially after November (Supplementary Fig. 9). The difference between the two sets of simulations (LessSCE minus ClimSCE) highlights the climate response to the 2024/2025 SCE reduction in western Eurasia. The reduced SCE firstly affects the atmosphere regionally by changing surface energy fluxes from November on (Supplementary Fig. 10)58. The reduced SCE over western Eurasia lowers surface albedo and increases net downward shortwave radiative flux (Supplementary Fig. 10). The resultant surface warming (Fig. 3a) heats the atmosphere via an enhanced upward longwave radiative flux (Supplementary Fig. 10). The atmospheric response has poleward warm advection (Fig. 3b) which, together with the diabatic heating, are balanced by cooling due to adiabatic ascent (Supplementary Fig. 11). The snow cover reduction-induced change in net surface radiative flux (i.e., shortwave and longwave combined) exceeds 25% of its wintertime climatological value.

Such radiative flux changes provide the heat source to trigger the downstream climatic impacts through propagating Rossby waves33. Prominent atmospheric wave trains from western Eurasia to North America begin to develop in late October, intensify in November, and stabilize in December (Fig. 3c,d; Supplementary Figs. 12–14). The atmospheric responses consist of two main wave trains, one along the subpolar jet and the other along the subtropical jet (Fig. 3c,d), and they merge over the North Pacific and continue propagating eastward to North America. The simulated atmospheric wave trains overall resemble those in the observed regression pattern (Fig. 2a,b), but mismatches do exist in, for example, the relative weight in the two wave trains. Those mismatches between the observations and the simulations are not unexpected because the simulations are designed to isolate the atmospheric circulation response to SCE forcing while the observational results may also contain the atmospheric circulation signals that cause the SCE to change.

In response to the SCE reduction, the simulated atmospheric circulation anomalies at the mature phase in DJ are characterized by a North Pacific cyclone and a western U.S. anticyclone (Fig. 3c,d), as in the observed regression patterns (Fig. 2a,b). This atmospheric circulation pattern drives a west-east dipole temperature pattern over North America (or the U.S.) (Fig. 3a), again consistent with observations (Supplementary Fig. 8). Over western North America, the high-pressure system (Fig. 3c) increases daily maximum temperatures (Fig. 3e), reduces relative humidity (Fig. 3g), and thereby enhances VPD (Fig. 3i). The increase in VPD in Southern California is about 33% of that observed in December 2024-January 2025 (i.e., 0.81 hPa in the model vs. 2.48 hPa in the gridMET dataset). In Southern California, although the strengthened southeasterlies (Fig. 3h) can intensify the Santa Ana winds, their magnitude is weaker than observed due possibly to the model limitation in accurately resolving the topography and simulating wind59. Additionally, the simulated precipitation reduction in Southern California (Fig. 3f) is notably weaker than in observations. As a result, the magnitude of the simulated FWI increase (Fig. 3j) is also weaker than observed. These results suggest that the western Eurasian SCE reduction can be one contributor to January wildfires in Southern California through VPD-related large-scale atmospheric circulation changes. The SCE impact on local precipitation and surface winds would be well worth studying in models that have a better representation of regional orographic features.

Dynamic mechanisms of the snow-fire teleconnection
Next, dynamic diagnostics are used to investigate the underlying mechanisms for the SCE reduction in western Eurasia to influence the wintertime large-scale remote atmospheric circulation response associated with Southern California wildfires and the North American dipole temperature anomaly. As previously discussed, the sustained reduction in snow cover over western Eurasia leads to enhanced upward wave activity flux (WAF), indicating vertical propagation of Rossby wave energy into the mid-to-upper troposphere (Supplementary Fig. 15), and is associated with the formation of a prominent Rossby wave source60 on its eastern flank (Fig. 4a). The simulated 200 hPa Rossby waves primarily propagate downstream, as shown by the horizontal WAF along the subpolar jet (Fig. 4c), with part of the wave energy refracted southeastward toward the subtropical jet. Then, the Rossby waves further propagate eastward along the North Pacific midlatitude jet61, intensify at the North Pacific midlatitude jet exit, and continue eastward to North America and Europe (Fig. 4a; Supplementary Fig. 15).

In DJ, the strong WAF in the North Pacific midlatitude jet exit (Figs. 4a and 4c) seems to imply a role of wave-mean flow interaction62. Over the midlatitude North Pacific storm track region, the eddy kinetic energy (EKE) response exhibits a marked increase (Fig. 4b). The impacts of the synoptic eddies on the mean flow at the 200 hPa level are then calculated via the quasi-geostrophic potential vorticity (QGPV) equation63 (Data and Methods). Changes in transient eddies induce a decrease in 200 hPa geopotential height over the central North Pacific and an increase in geopotential height on its eastern flank, consistent with the simulated 200 hPa (Fig. 3c) geopotential height responses. These results show that, in addition to the eastward-propagating Rossby waves, atmospheric feedbacks involving synoptic-scale transient eddies are also critical to the formation of the circulation patterns over the North Pacific-North America sector in response to the SCE reduction33. It is noteworthy that the atmospheric circulation response is comparatively weaker in November because the subtropical and the subpolar jets are considerably weaker and the wave-mean flow interaction is weaker64 (Supplementary Figs. 16 and 17). Also, the North Pacific midlatitude jet exit is displaced farther north in November than in DJ (Supplementary Fig. 16), limiting its influence in the western U.S.

Methods
Observational datasets
In this study, we employ two Canadian Fire Weather Index (FWI) datasets to represent potential fire danger69, which are widely used in western U.S. wildfire research29,30. These two FWI datasets are gridMET FWI70 and ERA5-based FWI71. The gridMET FWI provides daily high-spatial resolution (∼4 km, 1/24th degree) data over the contiguous U.S. from 1979 to the present. The ERA5-based FWI, produced by the Copernicus Emergency Management Service, is derived from ERA5 reanalysis data with a spatial resolution of 0.25° and daily temporal resolution, spanning from 1 January 1979 to the present. Detailed descriptions of these two datasets are available in Ref30. Given the historically low occurrence of wildfires in California during January, using the FWI is more suitable for statistical analysis than relying on the limited in-situ fire records. The correlation between gridMET FWI and ERA5-based FWI is presented in Supplementary Table 1. In addition, we use other meteorological variables from gridMET70 and ERA572 in our analysis.

In addition, we also use a new observational database of locations and sizes of individual fires in the western U.S. [Western U.S. MTBS-Interagency (WUMI) wildfire database]73. It includes wildfire events in the western U.S. that are ≥100 hectares (1 km²) in size from 1984 to the present, provided by a number of government sources.

Three Northern Hemisphere snow cover extent (SCE) satellite datasets are used in this study. The first dataset is the NOAA snow chart climate data record (CDR), archived and managed by the Rutgers University Global Snow Lab74. This dataset combines weekly (1966–May 1999) and daily (June 1999–Present) NOAA visible satellite-based SCE analyses to form a continuous record of snow coverage for the entire Northern Hemisphere, with a 24 km resolution. The second dataset is the Interactive Multisensor Snow and Ice Mapping System (IMS) snow cover data75, which covers February 1997 to present and is derived from a variety of data products, including satellite imagery and in situ data. This dataset provides daily SCE at a 24-km resolution. The third dataset is the MODIS monthly snow cover76 covering March 2000 to the present, with a spatial resolution of 0.05o x 0.05o. In this study, we analyze snow cover from 2000 to the present, which represents the period of overlap among the three datasets with relatively higher data accuracy77. The strong correlations among the three datasets during 2000–2024 demonstrate their overall consistency and reliability (Supplementary Table 1). We mainly use the NOAA CDR SCE dataset for analysis and as the prescribed snow forcing in model simulations.

We also use NASA Goddard's Global Surface Temperature Analysis (GISTEMP)78, which combines land surface air temperatures from GHCN-M version 4 with SSTs of the NOAA Extended Reconstructed SST Version 5 (ERSSTv5)79 into a comprehensive global surface temperature dataset. It spans 1880 to the present at monthly resolution, on a 2o x 2o latitude-longitude grid. In this study, the Niño3.4 index is defined by convention as ERSSTv5 SST anomalies averaged within 5°S–5°N and 170°W–120°W. Prior to further analysis, a partial regression is performed to remove the influence of ENSO from the observational datasets using Gram–Schmidt orthogonalization80.

CMIP6 historical and future scenario runs
Monthly data from 13 CMIP6 models (Supplementary Table 2) are used in this research. We use all models with available monthly FWI data81. In particular, we use each model’s available ‘r1i1p1f1’, ‘r2i1p1f1’, ‘r3i1p1f1’, ‘r4i1p1f1’ and ‘r5i1p1f1’ ensemble members, from two experiments: historical and SSP5-8.5. In total, 44 simulation runs are included in the analysis, each with FWI data for both the historical (1850–2014) and SSP5-8.5 (2015–2100) experiments.

Model simulations
To further validate the phenomena observed, we conduct a series of snow cover modification experiments with the Community Earth System Model 2.1 (CESM2.1)57. The atmospheric and land components use the f09 finite‐volume grid (0.9o latitude × 1.25o longitude), while the ocean and sea‐ice components use the gx1v6 displaced pole grid with a nominal resolution of ~1o × 1o. Our earlier studies investigating the climate effects of Eurasian SCE with an earlier CESM version demonstrated the model’s robustness, supporting its application in the present study33,37. In this study, the SCE forcing is prescribed over western Eurasia (10 °E-55 °E & 48 °N-60 °N) based on the NOAA snow chart CDR snow cover dataset, and model derived SCE is used everywhere else.

First, ten different initial conditions are selected at five-year intervals from a 100-year long control simulation. Then, for each initial condition, two sets of 12-member experiments with slightly perturbed atmospheric initial conditions that differ by 10-14 K82 are performed. Each run is integrated from October 1 through February 1 of the following year. The control set (“ClimSCE”) uses the climatological October–January SCE averaged over 2000–2025 as snow forcing, while the forced set (“LessSCE”) uses the observed SCE from October 2024 to January 2025. Thus, each ensemble begins with identical initial conditions and gradually diverges due to the imposed differences in snow boundary conditions. Each set consists of 120 simulations, and the response is defined as the difference between the ensemble means of the LessSCE and ClimSCE runs (LessSCE minus ClimSCE). In the model experiments, SCE forcing is prescribed from October through January to ensure a sustained and robust forcing, thereby facilitating a significant winter atmospheric circulation response33,37.

The land model used in this study is CLM5.083, which outperforms earlier versions (i.e., CLM4.5 and CLM4.0) in many aspects, including its enhanced ability to simulate snow-related hydrological processes. At each model time step, snowfall is permitted to accumulate at the surface. Melting of existing snow cover absorbs latent heat from the atmosphere and adds soil moisture to the land model. In our simulation, CLM5.0 is modified such that the simulated SCE over western Eurasia is replaced at each time step with a prescribed SCE for the specified snow season, interpolated from monthly values. As a result of this modification, energy and water are not explicitly conserved within the western Eurasia region in each ensemble84.

In this study, the idealized snow experiments were conducted with the fully coupled CESM2.1 model rather than an atmosphere-only (AGCM) model. While the model is fully coupled, our interpretation focuses on the atmospheric circulation response to the imposed western Eurasian snow cover anomalies, because the associated North Pacific SST response is quite weak (< 0.1 °C) (Supplementary Fig. 20a), and therefore unlikely to exert substantial feedback on the winter atmospheric circulation through North Pacific ocean–atmosphere coupling. Besides, the response of Arctic SIC is also negligible (Supplementary Fig. 20b).

Vapor pressure deficit (VPD)
Vapor pressure deficit (VPD) represents the difference between the actual vapor pressure and the saturation vapor pressure, reflecting the drying power of the air. It is calculated from daily maximum surface air temperature (𝑇𝑚𝑎𝑥, K) and surface relative humidity (RH, %) as follows30:
𝑒𝑠 = 611.2∗exp⁡(17.67∗(𝑇𝑚𝑎𝑥−273.15)/(𝑇𝑚𝑎𝑥−29.65)) (1)
VPD=𝑒𝑠(1−RH) (2)
where 𝑒𝑠 is the saturated vapor pressure in Pa.

Fire weather index (FWI)
Following the methodology used in the gridMET and ERA5 FWI products, we calculate the daily FWI response in the snow cover forcing experiments using the Canadian FWI system as described by Ref69. To calculate the FWI, we use the daily maximum surface air temperature (𝑇𝑚𝑎𝑥, K), surface relative humidity (RH, %), precipitation (Pr, mm/day) and surface wind speed (WS, m/s).

The FWI system comprises several indices and is calculated in three steps69,81. The first step contains computing three fuel moisture codes: Fine Fuel Moisture Code (FFMC), Duff Moisture Code (DMC), and Drought Code (DC), which represent the moisture content of different forest fuel layers. In the second step, the Initial Spread Index (ISI) and Buildup Index (BUI) are derived from the fuel moisture codes and surface wind speed to estimate the potential rate of fire spread and the available fuel for combustion. Finally, the Fire Weather Index (FWI) is computed by combining the ISI and BUI to produce a numerical rating of potential fire intensity. The FWI system is driven exclusively by meteorological inputs and does not account for factors such as vegetation type, ignition sources, or fire suppression efforts. For further method details, refer to Ref69 or Ref81.

Rossby wave activity flux (WAF)
WAF is used to study the propagation of Rossby waves. In this study, we apply the WAF formula proposed by Ref85 as a dynamical diagnostic tool for the three-dimensional propagation of Rossby waves. The horizontal component of the WAF, based on the monthly mean 200 hPa geostrophic wind, can be expressed as follows:
𝑊 = [𝑝/(2|𝑼|)] [𝑈(𝜓′𝑥^2−𝜓′𝜓′𝑥𝑥)+𝑉(𝜓′𝑥𝜓′𝑦−𝜓′𝜓′𝑥𝑦), 𝑈(𝜓′𝑥𝜓′𝑦−𝜓′𝜓′𝑥𝑦)+𝑉(𝜓′𝑦^2−𝜓′𝜓′𝑦𝑦)] (3)
where 𝑼(𝑈,𝑉) is climatological horizontal wind during 2000-2024, ψ′ is the monthly streamfunction anomaly derived from the 200 hPa geopotential height by the quasi-geostrophic approximation method, and x and y represent the longitude and latitude directions, respectively. The pressure, p, is standardized by 1000 hPa.

Horizontal eddy kinetic energy (EKE)
The horizontal EKE is calculated based on the perturbations of the 200 hPa horizontal wind speed63, and primarily reflects storm track changes:
EKE = ( ̅̅ 𝑢′′^2̅̅ + ̅̅ 𝑣′′^2̅̅ ) / 2 (4)
where u′′ and v′′ are zonal and meridional wind components derived from daily data with 2–8 day band-pass filtered fluctuations, respectively. The overbar denotes the monthly mean, and double-prime represents deviations of daily values from the corresponding long-term daily climatological mean.

Geopotential height tendency 𝝏𝒛⁄𝝏𝒕
The dynamical feedback of synoptic eddy activities onto 200 hPa time mean flow can be quantitatively calculated using the two-dimensional quasi-geostrophic potential vorticity (QGPV) equation63. The geopotential height tendency ∂z/∂t due to transient eddies is proportional to the inverse Laplacian of the convergence of the vorticity flux of transient eddies (π):
𝜕𝑧/𝜕𝑡 = 𝑓/𝑔 ∇^-2𝜋 (5)
where
𝜋 ≡ 1/(𝑎^2cos𝜃) (𝜕/𝜕𝜃 (1/cos𝜃 𝜕/𝜕𝜃 cos^2𝜃 𝑢′′𝑣′′) - 1/cos𝜃 𝜕^2/𝜕𝜆^2 𝑢′′𝑣′′) + 1/(𝑎^2cos^2𝜃) 𝜕^2/𝜕𝜆𝜕𝜃 cos𝜃(𝑢′′^2−𝑣′′^2) (6)
a is Earth’s radius, f is the Coriolis parameter, g is the gravitational acceleration, λ is longitude, and θ is latitude. The geopotential height tendency equation is used to evaluate the effect of transient eddies on geopotential height. u′′ and v′′ are zonal and meridional wind components derived from daily data with 2–8 day band-pass filtered fluctuations. The overbar denotes the monthly mean, and double-prime represents deviations of daily values from the corresponding long-term daily climatological mean.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "The manuscript presents an original and scientifically compelling framework linking western Eurasian snow cover anomalies to winter wildfire risk in Southern California. The observational–model synthesis is strong; however, the causal attribution could be strengthened by explicitly quantifying the contribution of competing drivers (e.g., ENSO, PDO, Arctic sea-ice anomalies) rather than describing them qualitatively. A sensitivity analysis or multi-model comparison section would help position the proposed mechanism relative to established teleconnections.\n\nMajor comments\n1. The CMIP6-based future risk interpretation remains somewhat speculative. The manuscript currently implies future predictability based on the mechanistic link, but the robustness of this signal under warming—given snow regime shifts, fire–fuel nonlinearities, and model spread—is not fully assessed. A short uncertainty framing (even if exploratory) would make the conclusions more aligned with Nature Communications standards.\n2. Validation of the fire weather signal would benefit from incorporating independent fire danger metrics beyond FWI and VPD (e.g., fuel moisture datasets, burned area residual analysis, or regional fire-weather classifications), particularly because winter fires are rare and observational record length is short.\n3. The authors should cite relevant work in which statistically derived fire weather indices have been compared against remotely sensed fire occurrence. In particular, Dhanurkar et al. (2024) demonstrated a strong spatiotemporal match between the Canadian Fire Weather Index and MODIS fire detections, which supports the methodological use of FWI as a proxy in regions or periods where burned area records are sparse. Including this strengthens the justification for using FWI in a low-fire-season context.\n\nMinor comments\n1. Figures 2 and 3 are dense and could benefit from clearer labeling and a more explicit visual guide to the causal sequence (e.g., schematic or pathway figure).\n2. Please clarify whether detrending procedures were applied symmetrically across predictor and response variables, and whether the correlation strength is sensitive to the detrending method.\n3. Some terminology (e.g., “bridge”, “pathway”) reads conceptual rather than mechanistic; consider aligning phrasing more closely with accepted atmospheric teleconnection nomenclature.\n4. For reproducibility, the exact boundaries of the western Eurasian domain used in each analysis should be stated consistently in both main text and figures (some places define it only in captions).\n5. Minor typographical and formatting edits needed, particularly in multi-panel figure captions and units formatting.\n\nReference\nDhanurkar, T., Budamala, V., & Bhowmik, R. D. (2024). Understanding the association between global forest fire products and hydrometeorological variables. Science of The Total Environment, 945, 173911.", "reviewer_2": "This study links the record-breaking winter 2025 wildfires in southern California to anomalously low snow cover over western Europe and proposes a “snow–fire bridge” teleconnection: reduced European snow triggers a downstream Rossby-wave train that is enhanced by wave–mean-flow interaction over the North Pacific, thereby creating meteorological conditions conducive to fire in southern California. The work provides a novel, physically based global perspective on the remote drivers of such extreme events. The manuscript is generally well organised and the conclusions are robust, but the following points need clarification or additional analyses to reach Nature Communications’ standards.\n\nComments\n1.This study used the FWI instead of burned area to investigate the remote drivers for the wildfires in South California. As shown in Fig. S3, the high FWI may provide a favorable condition for wildfires as in 2017 and 2024, but its impact is not necessarily inevitable, such as in 2011 and 2013. This should be clarified in the manuscript.\n2.Provide correlation coefficients (and significance levels) between western European snow anomalies and southern California winter FWI/burned area for 1920–2024 after excluding 2025, ideally with Monte-Carlo or bootstrap tests, to demonstrate that the teleconnection is not dominated by a single extreme year.\n3.Snow-cover anomalies are surface-based, yet the key wave response is shown in the mid-to-upper troposphere. Please add a longitude–height cross-section of wave-activity flux (WAF or EP-flux) along the wave path to demonstrate how the surface anomaly initiates upward-propagating Rossby waves. A brief dynamical discussion should also be included.\n4.The manuscript promotes Eurasian snow as a predictor. Quantify this: build a statistical model trained on 2001-2024 snow-cover indices and hindcast the 2025 event. Report cross-validated skill scores and the amplitude of the predicted versus observed Southern California VPD/FWI. If skill is modest, state this clearly.\n5.The analysis assumes the relationship is stationary over the observational period. Given climate change and the rapid warming of the Arctic, the teleconnection pathway's strength and structure may be non-stationary. This should be discussed as a key uncertainty and limitation.\n6.European snow anomalies explain only ~one-third of the local vapour-pressure deficit (VPD). Please discuss: What drives the remaining VPD increment (e.g., local subsidence warming, advection of dry/warm air, SST anomalies)? In addition, why 2017 exhibited high FWI and large burned area despite weak snow anomalies, to delineate the boundary conditions of the snow–fire bridge.\n7.The idealized snow experiments must be clearly detailed. Were they atmosphere-only (AGCM) runs with prescribed SSTs and snow? Or coupled? This has major implications for the interpretation.\n8.Unit is missing in Fig. S8"}, "author_response": "Responses to the reviews of the manuscript\n“A Snow-Fire Bridge Mechanism for 2025 Southern California Winter Wildfire”\nShizuo Liu, Shineng Hu, Richard Seager\nWe thank the reviewers for their time evaluating our work and for providing both encouraging feedback and constructive suggestions. Their insightful comments are vital in improving the clarity and rigor of our key findings. We have carefully addressed the reviewers’ suggestions, and the revised manuscript includes the following key changes:\n1. We examined and compared the lead-lag correlations between the Southern California December–January (DJ) FWI and different climate indices. We further constructed a multivariate linear regression model using major indices as predictors. These results indicate that, although some climate modes may contribute to DJ fire weather conditions in Southern California, western Eurasian SCE emerges as the dominant contributor (as suggested by Reviewers 1 and 2).\n2. We expanded the discussion on the implications of the snow–fire teleconnection for future wildfire activity under a warmer climate. Specifically, we discussed the applicability of this mechanism to externally forced climate changes, examined snow–fire correlations in CMIP6 simulations for both historical and future periods, and noted that fire–fuel nonlinearities may further complicate the future snow–fire relationship (as suggested by Reviewers 1 and 2).\n3. We clarified the interpretation and appropriate use of FWI as a fire danger indicator to ensure a more accurate understanding in the context of actual wildfire events (as suggested by Reviewers 1 and 2).\n4. We applied bootstrap tests to reinforce the robustness of our results and to demonstrate that the snow-fire relationship is not dominated by a single extreme year (as suggested by Reviewer 2).\n5. We constructed a statistical model based on the western Eurasian November–December SCE index (2000–2024) and applied it to hindcast the 2025 event. The model explains approximately 68% of the 2025 VPD anomaly and 40% of the FWI anomaly from a statistical perspective (as suggested by Reviewer 2).\n6. We incorporated additional independent fire danger metrics, including dead fuel moisture and the energy release component, to further strengthen the robustness of our main results (as suggested by Reviewer 1).\n7. We have included a schematic diagram, to better illustrate the dynamical processes of the snow-fire teleconnection proposed in our study (as suggested by Reviewer 1).\n8. We provided a more detailed description of the fully coupled CESM2.1 experiments used in this study and clarified our focus on the atmospheric pathway, given the quite weak oceanic responses (as suggested by Reviewer 2).\n9.We improved the figure captions and corrected the unit formatting to ensure accuracy and consistency (as suggested by Reviewers 1 and 2).\nOur point-by-point responses to the reviews are below (reviewers’ comments in black font, authors’ responses in blue font). Line numbers referenced in the responses are based on the track-changes version of the revised manuscript. Some texts from the revised manuscript are shown in quotes where relevant.\n[... full text of point-by-point responses omitted for brevity as per instructions to parse the provided text ...]"}, {"round": 2, "reviewer_comments": {"reviewer_1": "I have carefully examined the revised manuscript and the authors’ detailed point-by-point responses. I appreciate the substantial effort invested in addressing the concerns raised during the previous review. The additional analyses, clarifications, and expanded discussion have significantly strengthened the robustness, transparency, and overall presentation of the study.\n\nIn particular, the inclusion of bootstrap testing, independent fire danger metrics, the statistical hindcast experiment, and the expanded discussion on uncertainties and non-stationarity have addressed my main concerns. The revisions are thorough and appropriate.\n\nI am satisfied with the revision and support the manuscript for publication in its current form.", "reviewer_2": "My concerns have been satisfactorily addressed. I have no further comments."}, "author_response": "We thank the reviewer for the careful reassessment and for confirming that the previous concerns have been addressed.\n\nWe are glad that the reviewer’s concerns have now been satisfactorily addressed.\n\nWe thank the reviewer for this helpful suggestion. We have provided the main analysis code in the Code Ocean capsule and refactored the dynamical diagnostics component into reusable callable functions. Usage instructions have been added to the README to facilitate broader use."}]

Decision letter