Extreme weather events, rare yet profoundly impactful, are often accompanied by severe conditions. Increasing global temperatures are poised to exacerbate these events, resulting in greater human casualties, economic losses, and ecological destruction. Complex global climate interactions, known as teleconnections, can lead to widespread repercussions triggered by localized extreme weather. Understanding these teleconnection patterns is crucial for weather forecasting, enhancing safety, and advancing climate science. Here, we employ climate network analysis to uncover teleconnection patterns associated with extreme day-to-day temperature differences, including both extreme warming and cooling events occurring on a daily basis. Our study results demonstrate that the distances of significant teleconnections initially conform to a power-law decay, signifying a decline in connectivity with distance. However, this power-law decay tendency breaks beyond a certain threshold distance, suggesting the existence of long-distance connections. Additionally, we uncover a greater prevalence of long-distance connectivity among extreme cooling events compared to extreme warming events. The global pattern of teleconnections is, in part, likely driven by the mechanism of Rossby waves, which serve as a rapid conduit for inducing correlated fluctuations in both pressure and temperature. These results enhance our understanding of the multiscale nature of climate teleconnections and hold significant implications for improving weather forecasting and assessing climate risks in a warming world.

1.
J.
Tollefson
, “
Earth is warmer than it’s been in 125,000 years, says landmark climate report
,”
Nature
596
(7871),
171
172
(
2021
).
2.
A.
Morit
, “
Extreme heatwaves: Surprising lessons from the record warmth
,”
Nature
608
(7923),
464–465
(
2022
).
3.
P.
Kumar
, “Climate change and cities: Challenges ahead,”
Front. Sustain. Cities
3
(
2021
).
4.
K.
Abbass
,
M. Z.
Qasim
,
H.
Song
,
M.
Murshed
,
H.
Mahmood
, and
I.
Younis
, “
A review of the global climate change impacts, adaptation, and sustainable mitigation measures
,”
Environ. Sci. Pollut. Res.
29
,
42539
42559
(
2022
).
5.
W. K.
Lau
and
K.-M.
Kim
, “
The 2010 Pakistan flood and Russian heat wave: Teleconnection of hydrometeorological extremes
,”
J. Hydrometeorol.
13
,
392
403
(
2012
).
6.
N.
Boers
,
B.
Goswami
,
A.
Rheinwalt
,
B.
Bookhagen
,
B.
Hoskins
, and
J.
Kurths
, “
Complex networks reveal global pattern of extreme-rainfall teleconnections
,”
Nature
566
,
373
377
(
2019
).
7.
G. T.
Walker
, “
Correlations in seasonal variations of weather—A further study of world weather
,”
Monthly Weather Rev.
53
(6),
252
254
(
1925
).
8.
H. A.
Bridgman
and
J. E.
Oliver
,
The Global Climate System: Patterns, Processes, and Teleconnections
(
Cambridge University Press
,
2014
).
9.
Z.
Liu
and
M.
Alexander
, “
Atmospheric bridge, oceanic tunnel, and global climatic teleconnections
,”
Rev. Geophys.
45
(2),
RG2005
, https://doi.org/10.1029/2005RG000172 (
2007
).
10.
H.
Tu
,
S.
Wang
,
J.
Meng
,
Y.
Zhang
,
X.
Chen
,
D.
Chen
, and
J.
Fan
, “
Eigen microstate analysis unveils climate dynamics
,”
Sci. China Phys. Mech. Astron.
68
,
240511
(
2025
).
11.
J.
Fan
,
J.
Meng
,
J.
Ludescher
,
X.
Chen
,
Y.
Ashkenazy
,
J.
Kurths
,
S.
Havlin
, and
H. J.
Schellnhuber
, “
Statistical physics approaches to the complex earth system
,”
Phys. Rep.
896
,
1
84
(
2021
).
12.
J.
Meng
,
J.
Fan
,
Y.
Ashkenazy
, and
S.
Havlin
, “
Percolation framework to describe El Niño conditions
,”
Chaos
27
,
035807
(
2017
).
13.
J.
Meng
,
J.
Fan
,
Y.
Ashkenazy
,
A.
Bunde
, and
S.
Havlin
, “
Forecasting the magnitude and onset of El Niño based on climate network
,”
New J. Phys.
20
,
043036
(
2018
).
14.
J.
Fan
,
J.
Meng
,
Y.
Ashkenazy
,
S.
Havlin
, and
H. J.
Schellnhuber
, “
Network analysis reveals strongly localized impacts of El Niño
,”
Proc. Natl. Acad. Sci. U.S.A.
114
,
7543
7548
(
2017
).
15.
N.
Boers
,
B.
Bookhagen
,
H. M.
Barbosa
,
N.
Marwan
,
J.
Kurths
, and
J.
Marengo
, “
Prediction of extreme floods in the eastern Central Andes based on a complex networks approach
,”
Nat. Commun.
5
,
5199
(
2014
).
16.
N.
Boers
,
R. V.
Donner
,
B.
Bookhagen
, and
J.
Kurths
, “
Complex network analysis helps to identify impacts of the El Niño Southern Oscillation on moisture divergence in South America
,”
Clim. Dyn.
45
,
619
632
(
2015
).
17.
Q. Y.
Feng
and
H.
Dijkstra
, “
Are North Atlantic multidecadal SST anomalies westward propagating?
Geophys. Res. Lett.
41
,
541
546
, https://doi.org/10.1002/2013GL058687 (
2014
).
18.
H.
Hersbach
,
B.
Bell
,
P.
Berrisford
,
S.
Hirahara
,
A.
Horányi
,
J.
Muñoz-Sabater
,
J.
Nicolas
,
C.
Peubey
,
R.
Radu
,
D.
Schepers
et al., “
The ERA5 global reanalysis
,”
Q. J. R. Meteorol. Soc.
146
,
1999
2049
(
2020
).
19.
B.
Harvey
,
L.
Shaffrey
, and
T.
Woollings
, “
Equator-to-pole temperature differences and the extra-tropical storm track responses of the CMIP5 climate models
,”
Clim. Dyn.
43
,
1171
1182
(
2014
).
20.
S. E.
Perkins-Kirkpatrick
and
P. B.
Gibson
, “
Changes in regional heatwave characteristics as a function of increasing global temperature
,”
Sci. Rep.
7
,
12256
(
2017
).
21.
E.
Rousi
,
K.
Kornhuber
,
G.
Beobide-Arsuaga
,
F.
Luo
, and
D.
Coumou
, “
Accelerated western European heatwave trends linked to more-persistent double jets over Eurasia
,”
Nat. Commun.
13
,
3851
(
2022
).
22.
D.
Sornette
and
G.
Ouillon
, “
Dragon-kings: Mechanisms, statistical methods and empirical evidence
,”
Eur. Phys. J. Spec. Top.
205
,
1
26
(
2012
).
23.
M.
Sachs
,
M.
Yoder
,
D.
Turcotte
,
J.
Rundle
, and
B.
Malamud
, “
Black swans, power laws, and dragon-kings: Earthquakes, volcanic eruptions, landslides, wildfires, floods, and SOC models
,”
Eur. Phys. J. Spec. Top.
205
,
167
182
(
2012
).
24.
S.
Wang
,
J.
Meng
, and
J.
Fan
, “
Exploring the intensity, distribution and evolution of teleconnections using climate network analysis
,”
Chaos
33
,
103127
(
2023
).
25.
Y.
Zhang
,
J.
Fan
,
X.
Chen
,
Y.
Ashkenazy
, and
S.
Havlin
, “
Significant impact of Rossby waves on air pollution detected by network analysis
,”
Geophys. Res. Lett.
46
,
12476
12485
, https://doi.org/10.1029/2019GL084649 (
2019
).
26.
W.
Cai
,
M.
Lengaigne
,
S.
Borlace
,
M.
Collins
,
T.
Cowan
,
M. J.
McPhaden
,
A.
Timmermann
,
S.
Power
,
J.
Brown
,
C.
Menkes
et al., “
More extreme swings of the south pacific convergence zone due to greenhouse warming
,”
Nature
488
,
365
369
(
2012
).
27.
R.
Goyal
,
A.
Sen Gupta
,
M.
Jucker
, and
M. H.
England
, “
Historical and projected changes in the southern hemisphere surface westerlies
,”
Geophys. Res. Lett.
48
,
e2020GL090849
, https://doi.org/10.1029/2020GL090849 (
2021
).
28.
A. A.
Tsonis
and
K. L.
Swanson
, “
Topology and predictability of El Nino and La Nina networks
,”
Phys. Rev. Lett.
100
,
228502
(
2008
).
29.
J. F.
Donges
,
Y.
Zou
,
N.
Marwan
, and
J.
Kurths
, “
Complex networks in climate dynamics: Comparing linear and nonlinear network construction methods
,”
Eur. Phys. J. Spec. Top.
174
,
157
179
(
2009
).
30.
T. M.
Lenton
,
H.
Held
,
E.
Kriegler
,
J. W.
Hall
,
W.
Lucht
,
S.
Rahmstorf
, and
H. J.
Schellnhuber
, “
Tipping elements in the Earth’s climate system
,”
Proc. Natl. Acad. Sci. U.S.A.
105
,
1786
1793
(
2008
).
31.
T. M.
Lenton
,
J.
Rockström
,
O.
Gaffney
,
S.
Rahmstorf
,
K.
Richardson
,
W.
Steffen
, and
H. J.
Schellnhuber
, “
Climate tipping points—Too risky to bet against
,”
Nature
575
,
592
595
(
2019
).
32.
C. A.
Nobre
,
G.
Sampaio
,
L. S.
Borma
,
J. C.
Castilla-Rubio
,
J. S.
Silva
, and
M.
Cardoso
, “
Land-use and climate change risks in the Amazon and the need of a novel sustainable development paradigm
,”
Proc. Natl. Acad. Sci. U.S.A.
113
,
10759
10768
(
2016
).
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