# Climate Shocks and U.S. Bank Stability

Olivier Damette, Maxime Fajeau, Rémi Generoso, Clément Mathonnat

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Olivier Damette, Maxime Fajeau, Rémi Generoso, Clément Mathonnat. Climate Shocks and U.S. Bank Stability. 2025. hal-05396142

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CLIMATE SHOCKS AND U.S. BANK STABILITY

Olivier Damette$^{abc}$

Rémi Generoso$^{bd}$

Maxime Fajeau$^d$ ☑

Clément Mathonnat$^a$

December 3, 2025

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Latest version here

**Abstract**

This paper investigates the effects of systemic climate variability on U.S. banking stability using the El Niño–Southern Oscillation (ENSO) as a quasi-natural experiment. In contrast to studies focusing on rare, localized natural disasters, we examine how persistent and spatially heterogeneous ENSO-induced climate anomalies—especially those associated with the often-overlooked *La Niña* phase—affect banks across the continental United States. ENSO is the most influential source of interannual climate variation on Earth and provides a compelling setting to study the transmission of exogenous physical risks to the financial sector. We construct a 30-year quarterly panel of over 800,000 bank-quarter observations (1994–2023), combining detailed financial data with geolocated branch networks and high-resolution teleconnection estimates of local temperature anomalies. Our empirical strategy combines three key elements: a regime-based climate shock identification grounded in recent climate science, a granular spatial matching of institutions to localized exposure, and a dynamic panel framework based on local projections. Our results show that strong *La Niña* shocks reduce the distance to default by roughly 20%, with effects peaking between 7 and 11 quarters after the shock. These disruptions operate primarily through rising credit risk, lower profitability, and weaker solvency—particularly in banks with large real estate exposure, broad but climate-sensitive geographic footprints, and sizable balance sheets. These findings underscore the need for prudential regulation to incorporate granular, forward-looking metrics of physical climate risk, especially as ongoing climate change is expected to increase the frequency and intensity of ENSO events.

**JEL Classification** Banks · Climate · ENSO · Stability

**Keywords** G21 · Q54 · G01 · C23

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

<footnote>$^a$University of Lorraine, BETA-CNRS, France.</footnote>

<footnote>$^b$Climate Economic Chair, Paris Dauphine University, France.</footnote>

<footnote>$^c$Macroeconomic and Forecasting Chair, UQAM, Montreal, Canada.</footnote>

<footnote>$^d$University of Lille, LEM-CNRS, France.</footnote>

<footnote>Corresponding author: maxime.fajeau@univ-lille.fr</footnote>
# 1 Introduction

Climate change poses significant threats to society and economic performances, with potentially profound implications for the financial sector (Carlton and Hsiang, 2016; Dell et al., 2012; Burke et al., 2015; Kotz et al., 2021, 2024; Bilal and Känzig, 2024). The array of physical risks—droughts, hurricanes, floods, and heat waves—generates deep uncertainty about their financial consequences. Central banks and prudential regulators have coined the term “Green Swan” to describe these complex, potentially systemic risks (Carney, 2015; Bolton et al., 2020; NGFS, 2020; Batten et al., 2016), which encompass both acute weather events and gradual climate shifts that can destabilize banking systems through multiple transmission channels. While transition risks have been widely studied, recent reviews underscore a critical gap: physical climate risks—especially those arising from persistent climatic variability rather than discrete disasters—remain insufficiently understood at the banking level (de Bandt et al., 2024).

This gap underscores the need to better quantify climate-induced financial risks and to clarify how they trans-
mit through the banking system. From a policy perspective, this is essential for central banks and prudential
regulators aiming to design forward-looking macroprudential frameworks that account for physical climate
risks (NGFS, 2020; Chabot and Bertrand, 2023). Banking stability is a cornerstone of economic resilience,
underpinning capital allocation, credit provision, and risk sharing. Yet climate change increasingly disrupts
these functions: temperature and precipitation anomalies weaken economic performance (Dell et al., 2012;
Kotz et al., 2024) and elevate credit risk in exposed sectors such as agriculture, energy, and real estate (Co-
lacito et al., 2019). As climate variability intensifies and long-standing weather patterns shift in persistent
and geographically widespread ways (Diffenbaugh et al., 2017; Bolton et al., 2020), enhancing banks’ capac-
ity to absorb recurrent climate shocks has become a central challenge for both micro- and macroprudential
policy (Brei et al., 2024).

**Research question.** This paper addresses three core questions: To what extent do climate shocks affect banking stability? Through which channels do they propagate into banks’ balance sheets? And what bank-level characteristics enhance resilience? To answer these questions, we leverage the quasi-random variation induced by the *El Niño*–Southern Oscillation (ENSO)—the most important driver of global climate variability at interannual timescales (Cai et al., 2018; Timmermann et al., 2018; Yeh et al., 2018). ENSO events (*El Niño* and *La Niña*) generate distinct systematic and recurrent weather anomalies across the United States (U.S.), providing an ideal empirical setup to identify the causal effects of climate shocks on financial institutions (An et al., 2020; Okumura and Deser, 2010). Their macroeconomic significance is well documented: the 1982–83 and 1997–98 *El Niño* episodes resulted in estimated global income losses of $4.1 trillion and $5.7 trillion, respectively (Callahan and Mankin, 2023). In the U.S., the 2010–2011 *La Niña* event alone caused over $10 billion in drought damages (Hoerling et al., 2013; Smith and Katz, 2013), with particularly severe impacts in agricultural regions such as Texas, California, and the Great Plains—areas where local banks are heavily exposed through farm credit and real estate lending.

As a result, this paper proposes a new empirical setup to assess the impact of climate physical risks on U.S. banking stability. Instead of considering rare and localized natural disasters, we study the impact of ENSO-induced climate shocks, while accounting explicitly for the asymmetric effects of its *El Niño* and *La Niña* phases and its influence on local weather conditions through teleconnections. Based on an empirical strategy providing a regime-based climate shock identification grounded in advances from climate sciences, a fine-grained spatial matching of financial institutions to localized exposures, and a dynamic estimation framework adapted to persistent, non-absorbing climate shocks, our results challenge recent findings from De Marco and Limodio (2023), who only consider the global impact of ENSO's signal on U.S. banking system in a static panel context—with a narrow focus on *El Niño* events—and assess teleconnections through an infographic approach.

**Reframing physical risk.** Existing studies on physical risks and banking stability yield mixed findings. Some suggest limited effects of weather-related disasters on U.S. banks (Noth and Schüwer, 2023), while others document more persistent disruptions: droughts have been shown to impair the financial condition
of publicly traded banks in the U.S. (Özsoy et al., 2025), and hurricanes have been linked to heightened bank default risk in the Caribbean (Brei et al., 2019). Others still emphasize the banking sector's resilience, citing post-disaster aid and increased loan demand as stabilizing forces (Blickle et al., 2023). These diverging results reflect not only heterogeneity in local exposures and responses, but also key empirical limitations the present paper addresses. Disaster-based approaches typically focus on rare, extreme tail risks—sudden losses, asset destruction, and emergency transfers—whose impacts are highly conditional on local vulnerabilities shaped by endogenous factors such as infrastructure quality, insurance coverage, and policy response (Klomp, 2014; Cortés and Strahan, 2017; Rehbein and Ongena, 2022). Thus, this literature often overlooks the bulk of the distribution, where persistent macro-financial costs accumulate through slow-moving climate anomalies—such as recurrent heat and drought during *La Niña* episodes in the U.S.—that can gradually erode borrower cash flows, raise input costs for energy and water, depress collateral values, and lead to a gradual rise in loan defaults.

Consistent with evidence of underreaction to slow-moving climate risks (Hong et al., 2019; DellaVigna and Pollet, 2007), this paper takes a different approach by shifting the focus from isolated natural disasters to systemic climate variability. Taking advantage of the quasi-random variation induced by ENSO, we study how recurrent but non-catastrophic anomalies—such as sustained heat and moisture stress—affect banks through credit risk, performances, and profitability. Focusing on ENSO-induced climate shocks may be particularly relevant as climate change progresses since it allows us to capture both acute and gradual manifestation of climate stress, from crop failures and wildfire risk to eroding cash flows and asset quality (Dell et al., 2012; Burke et al., 2015; Kotz et al., 2021, 2022). Crucially, ENSO episodes affect broad swaths of the U.S. simultaneously, enabling the identification of financial effects that are otherwise difficult to detect using localized events. As recent work has shown, the aggregate effects of ENSO-induced climate shocks on economic growth are large and persistent (Callahan and Mankin, 2023), reinforcing the need for an in-depth assessment of their financial consequences for the U.S. banking sector.

**Empirical strategy.** This paper introduces a novel empirical framework to quantify how climate shocks affect U.S. banking stability, bridging a uniquely granular dataset on bank performance with state-of-the-art climate science methods for capturing ENSO dynamics and localized exposure (Zhang et al., 2019; Liu et al., 2023). We construct a 30-year quarterly panel covering nearly 10,000 commercial banks and over 80,000 branches from 1994Q1 to 2023Q2, yielding more than 800,000 bank-quarter observations. This dataset combines consistent financial reporting from Call Reports with detailed geographic information from the Summary of Deposits, enabling precise spatial attribution of climate exposure based on the full branch network of each institution. We track key measures of bank stability and performance—including Z-scores, return on assets, return on equity, non-performing loans, and foreclosures—across heterogeneous institutional structures and regional contexts.

To identify the effects of climate shocks on banking stability, we exploit variations in intensity and duration of ENSO cycles, considering both of its *El Niño* and *La Niña* phases. They induce distinct but systematic patterns of climate anomalies in the U.S. Rather than focusing on rare, localized natural disasters, we examine ENSO-induced temperature and precipitation anomalies—systematic, recurrent deviations from climatic norms—captured through high-resolution teleconnection patterns across the contiguous U.S. (Liu et al., 2023). These localized anomalies are then aggregated into bank-specific exposures using deposit-weighted averages across all branch locations. We estimate the dynamic impact of these shocks using panel local projections (Jordà, 2005, 2023; Jordà and Taylor, 2025), a flexible approach that allows for horizon-specific responses with minimal parametric assumptions.

**Main empirical findings.** We demonstrate that ENSO-related climate shocks significantly destabilize U.S. banks, with strong *La Niña* episodes reduce the distance to default by approximately 20%. We also show that *La Niña* phases exert substantially larger adverse impacts than *El Niño* phases—up to 2.5 times greater—driven primarily by temperature anomalies rather than precipitation. Strong *La Niña* phases lead to a 17% increase in non-performing loans and a 40% rise in foreclosure rates, with effects peaking between 7 and 11 quarters after the initial shock. These dynamics reflect a coherent financial transmission pattern:
climate-induced costs first raise cost-to-income ratios, followed by a deterioration in loan quality and an in-
crease in asset liquidation through foreclosures—consistent with regulatory delays in non-performing loan
classification and the time-intensive nature of foreclosure proceedings. We further show that temperature
anomalies are the dominant transmission channel from ENSO to banking instability, reflecting reduced
agricultural productivity (Dell et al., 2012; Addoum et al., 2020; Atems and Jones, 2020), increased water
stress, deterioration of natural amenities (Desmet and Rossi-Hansberg, 2015; Glaeser, 1998), and direct
effects on labor productivity (Burke et al., 2015; Kalkuhl and Wenz, 2020; Kotz et al., 2021). Finally, an
analysis of bank-level resilience factors further shows that institutions with high exposure to real estate lend-
ing, limited geographic diversification, and larger balance sheets are significantly more vulnerable to these
climate-induced shocks.

**Key contributions.** Although the economics literature has often focused on *El Niño* episodes, *La Niña* is frequently treated as a symmetrical but less important counterpart—sometimes even used as a placebo in empirical designs centered on *El Niño* shocks. In contrast, we show that *La Niña* generates substantially larger impacts than *El Niño* with significant implications for U.S. banking stability, contradicting this placebo test assumption. For instance, De Marco and Limodio (2023), the only other paper in the literature—to our knowledge—linking ENSO to U.S. banks, use an annual panel covering 6,000 banks over the period 1980-2019 to investigate how ENSO-induced climate shocks affects the U.S. banking institutions, considering the global impact of ENSO’s signal in a static panel context—with a narrow focus on *El Niño* events—and assess teleconnections through a coarse graphic approach. They show that strong *El Niño* events are associated with lower house prices and mortgage lending in counties experiencing temperature increase. At the bank-level, they additionally show a decrease in total assets and lending for banking institutions with high exposure to the counties that experience a temperature increase during *El Niño* events.

Our study significantly extends the analysis in De Marco and Limodio (2023) along four key dimensions. First, in terms of research scope, we focus specifically on banking stability, examining the distinct financial transmission channels—namely credit risk and performance metrics—through which ENSO events affect U.S. banks. We pay particular attention to the asymmetric impacts of both *El Niño* and *La Niña* phases. This distinction is increasingly relevant in light of mounting climate science evidence that *La Niña* episodes are becoming more frequent and persistent, including unprecedented multi-year sequences such as 2020–2022 (Geng et al., 2023; Chen et al., 2025). These prolonged *La Niña* phases have been directly linked to the intensification of North American megadroughts (Williams et al., 2022), with cumulative impacts on agriculture, water availability, and regional economies (Wang et al., 2007; Seager et al., 2022). Ignoring such dynamics risks underestimating both the systemic nature of physical climate risk and the potential for macro-financial disruptions arising from sustained climatic anomalies (Seneviratne et al., 2021). Second, on the climate science front, we construct spatially explicit, high-resolution temperature and precipitation teleconnection maps across the contiguous U.S., rather than relying on coarse NOAA exposure classifications. This enables a regime-based identification of climate shocks, grounded in recent advances from climatology, and allows for a more precise measurement of the heterogeneous spatial footprint of ENSO-induced anomalies. Third, regarding banking data, we construct a unique dataset that includes nearly 10,000 commercial banks observed at quarterly frequency over a 30-year period—rather than annually—and incorporates information on more than 80,000 branches. This allows for high-resolution spatial matching of each bank’s exposure based on its complete branch network, rather than solely relying on headquarter locations. This granularity is essential for capturing how localized climate anomalies transmit into institution-level financial stress. Lastly, rather than relying on static panel designs focused exclusively on the impacts of *El Niño*, we adopt a dynamic estimation framework tailored to the persistent, non-absorbing nature of climate shocks. This framework exploits both the spatial and temporal variation in ENSO phases to model the dynamic structure of *El Niño* and *La Niña* events, uncover asymmetries in their effects on U.S. banking stability, and capture nonlinearities in the response that depend not only on the intensity of local climate anomalies but also on bank-specific resilience characteristics. Our empirical strategy builds on the local projections approach, now widely used in recent research on the macroeconomic effects of ENSO, and allows for tracing multi-horizon responses without imposing strong structural assumptions. Taken together, these elements
yield new and generalizable evidence on how slow-moving but systematic climate variability transmits into
financial stress—substantially extending the findings of De Marco and Limodio (2023).

As a result, this paper advances the literature on climate and financial stability by providing the first compre-
hensive analysis of how systemic climate variability—rather than localized disasters—affects the stability
of the banking sector. The contribution is threefold. First, the analysis addresses a fundamental yet underex-
plored dimension of physical climate risk: persistent, non-catastrophic variability that gradually alters eco-
nomic conditions across large regions. By focusing on large-scale regime shifts—proxied by ENSO—this
approach uncovers widespread, multi-quarter financial disruptions that are often missed by disaster-based
frameworks. Second, the study makes a methodological contribution by bridging recent advances from cli-
mate science with a uniquely granular dataset on U.S. banking activity. A near-universe panel of geolocated
bank data is combined with high-resolution ENSO indices and nonlinear teleconnection maps to construct
branch-level, deposit-weighted exposures to localized temperature anomalies. This design enables a precise
investigation of how climate shocks generate financial vulnerability, offering the first in-depth assessment of
ENSO's banking consequences—with particular emphasis on the often-overlooked *La Niña* phase. Finally,
the paper provides new evidence on both the timing and magnitude of climate-financial transmission. Using
panel local projections, the analysis traces the dynamic unfolding of financial stress—from rising cost-to-
income ratios, to delayed deterioration in loan performance, and ultimately increased foreclosures—thus
offering a scalable framework for identifying and quantifying systemic climate risk in financial systems.

**Policy Implications.** Our findings have direct implications for financial supervision and climate risk assessment. The results highlight the importance of integrating physical climate variability—not just extreme weather events or transition scenarios—into prudential frameworks. The spatially heterogeneous nature of these effects suggests that risk exposure depends not only on business models but also on geography: banks operating in climate-sensitive regions are particularly vulnerable. This calls for geographically disaggregated supervisory tools and stress testing methodologies capable of capturing both acute and persistent forms of physical risk. This concern is heightened by mounting climate science evidence that persistent *La Niña* episodes are becoming more frequent and that ongoing global warming may further amplify ENSO dynamics, increasing the frequency and intensity of these climate shocks and their associated systemic financial risks (Cai et al., 2018, 2021; Lopez et al., 2022; Chen et al., 2025).

**Outline.** The remainder of the paper is structured as follows. Section 2 presents the ENSO phenomenon from a U.S. perspective and derives testable hypotheses regarding the ENSO-bank stability nexus. Section 3 describes the dataset. Section 4 outlines the identification strategy and econometric methodology. Section 5 presents the baseline results. Section 6 and Section 7 assess the heterogeneity in local climate exposure and the role of bank-specific resilience factors, respectively. Section 8 provides robustness checks. The final section concludes.

2 ENSO and U.S. Climate Framework

The El Niño–Southern Oscillation (ENSO) is the most influential driver of interannual climate variabil-
ity worldwide. Originating from ocean–atmosphere interactions in the tropical Pacific (see Figure 1), it
alternates between two distinct phases—*El Niño* and *La Niña*—that generate asymmetric and spatially het-
erogeneous climate anomalies across regions (Smith and Ubilava, 2017; Generoso et al., 2020; Liu et al.,
2023). These persistent shifts in temperature and precipitation can affect agricultural yields, water availabil-
ity, and infrastructure stress, with far-reaching consequences for regional economic performance (Callahan
and Mankin, 2023). The recurrence and quasi-random timing of ENSO events make them a valuable quasi-
natural experiment for identifying the dynamic effects of systemic climate variability (Damette et al., 2024).
In this context, understanding the differentiated regional impacts of ENSO is essential to assess how such
climate shocks transmit into banking stability through geographically heterogeneous exposure.
<figure><img src="image_4.png" /><figcaption>**Figure 1:** Spatial patterns of sea surface temperature anomalies</figcaption></figure>

Note: Composites of sea surface temperature (SST) anomalies during months characterized by strong magnitude ($r > 2$) of *El Niño* (A) and *La Niña* (B) events. These composites were calculated by averaging the SST anomalies at each grid point for the selected months. The anomalies are defined relative to a climatological baseline and represent deviations in SST from normal conditions over the tropical Pacific Ocean. Author's calculation following Zhang et al. (2019).

**El Niño impacts on the U.S.** El Niño events, such as those in 1997–1998 and 2015–2016, are typically associated with increased precipitation in the southern U.S., temporarily alleviating drought conditions. However, this added rainfall often leads to flooding and strains infrastructure ill-equipped to handle sudden water surges (Trenberth and Hoar, 1997; Swain et al., 2017). In the agricultural sector, *El Niño* phases generally yield favorable outcomes for crops in the Cornbelt, with above-average corn and soybean yields observed during such periods due to milder growing conditions (Atems and Jones, 2020). Additionally, *El Niño* events tend to suppress Atlantic hurricane activity due to altered atmospheric dynamics, reducing the risk of storm-related agricultural damage (Klotzbach, 2008). From a financial perspective, these milder conditions can temporarily ease borrower cash-flow pressures and collateral stress in some regions, though localized flooding can still impair asset quality where infrastructure exposure is high.

**La Niña impacts on the U.S.** In contrast to the milder effects of *El Niño*, *La Niña* phases—marked by cooler Pacific sea surface temperatures (see Figure 1, panel B)—consistently produce more severe and prolonged climatic disruptions in the U.S. By shifting the Pacific jet stream northward, *La Niña* suppresses precipitation across the southern states and amplifies drought, heatwaves, and wildfire risk. The 2010–2012 back-to-back *La Niña* episodes triggered one of the most widespread droughts since the 1930s, affecting roughly 65% of the continental U.S. In Texas alone, 2011 agricultural losses exceeded $7.6 billion, alongside over 3 million acres of wildfire damage—including the Bastrop County Complex Fire, which destroyed more than 1,600 homes (Texas Forest Service, 2012). The following year, drought conditions spread across the Midwest, reducing corn and soybean yields in over 90% of the Cornbelt (Rippey, 2015; Atems and Jones, 2020). According to the National Climatic Data Center, 2012 was the warmest *La Niña* year on record.

More recently, the 2020–2023 “triple-dip” *La Niña* underscores the potential for persistent economic and environmental stress. Extended heat and moisture deficits intensified wildfire risks in the West and exacerbated thermal and hydric stress across agricultural zones (Westerling, 2016). Climate science suggests that *La Niña* episodes are likely to grow more frequent and extreme under global warming, further amplifying disruptions to temperature, precipitation, and fire regimes (Fasullo et al., 2018). These persistent anomalies align closely with the key transmission channels relevant for banking: heightened agricultural income volatil-
ity, degradation of natural amenities, and sustained collateral devaluation in real-estate-intensive regions.

**Transmission to banking stability.** The deterioration of property values due to droughts, wildfires, and water scarcity can undermine the collateral backing real estate loans, increasing default risks and reducing banks' overall lending capacity (Bernstein et al., 2019; Baldauf et al., 2020; Keenan et al., 2018). These climate-induced declines in asset valuations are especially acute in regions heavily dependent on agriculture or natural resources, where *La Niña*-related climate anomalies can erode borrowers' repayment capacity through declines in income and economic activity. Moreover, adaptation costs—such as rising insurance premiums, infrastructure upgrades, or increased competition for water—further strain borrowers and, by extension, lenders, particularly in real estate financing. As banks reassess the creditworthiness of assets increasingly exposed to ENSO-related shocks, the conditions associated with *La Niña* may exacerbate the vulnerability of real estate loan portfolios, with potential spillovers to broader measures of banking stability including credit risk, profitability, and solvency. Consistent with evidence that, in advanced economies with mature financial systems, climate shocks primarily affect banks through deteriorations in credit quality and operating performance—rather than through liquidity constraints (Noth and Schüwer, 2023; Brei et al., 2019; Chabot and Bertrand, 2023)—this study focuses on non-performing loans, foreclosures, return on assets (ROA), and equity-to-assets as core margins of banking vulnerability.

**Testable Hypotheses.** Guided by prior literature, this paper empirically tests four hypotheses concerning the relationship between ENSO variability and U.S. banking stability:

*H1 (General).* ENSO variability increases banking instability, as reflected in lower Z-scores.

*H2 (Phase dependency).* *La Niña* episodes have significantly more adverse effects on bank stability than *El Niño* phases.

*H3 (Credit risk channel).* ENSO shocks raise credit risk, leading to increases in non-performing loans and foreclosure rates.

*H4 (Profitability/solvency channel).* ENSO shocks weaken bank performance, resulting in lower profitability and solvency (measured by ROA, equity-to-assets, and cost-to-income ratios).

# 3 Data

## 3.1 ENSO data

**ENSO signal.** ENSO, the dominant mode of interannual climate variability on earth, exhibits substantial complexity in both its positive (*El Niño*) and negative (*La Niña*) phases. While traditionally viewed as mirror images of each other, these phases show pronounced asymmetry in spatial pattern, amplitude, and duration. Strong *El Niño* events often manifest toward the eastern Pacific, whereas strong *La Niña* events tend to peak centrally. Moreover, *El Niño* events are commonly shorter-lived and can reach greater magnitude, while *La Niña* events frequently persist longer and display a broader range of spatial configurations. Such complexity, often referred to as “ENSO diversity,” has implications for atmospheric circulation patterns, global teleconnections, and regional precipitation and temperature (Capotondi et al., 2015; Takahashi and Dewitte, 2016; Timmermann et al., 2018).

Conventional indices, such as the *Niño3.4* and *Oceanic Niño Index* (ONI), have advanced our understanding but largely capture ENSO variation along a single dimension and often assume a Gaussian distribution of sea surface temperature anomalies. These assumptions may obscure critical nuances, particularly given the skewed and evolving nature of *La Niña* events. Recent studies emphasize the necessity of accounting for both the overall intensity and the spatial structure of sea surface temperature anomalies across the equatorial Pacific to fully represent ENSO's complex flavors and asymmetries (Okumura et al., 2011; Cai et al., 2020).

Building on this recent knowledge, we calculate the Unified ENSO Complex Index (UECI) proposed by Zhang et al. (2019). The UECI integrates both the amplitude and longitudinal distribution of ENSO-related
<figure><img src="image_5.png" /><figcaption>Figure 2: Temporal profile of ENSO</figcaption></figure>

Note: An *El Niño* or *La Niña* event is determined when the magnitude *r* of the Unified ENSO Complex Index (UECI) exceeds 0.5 for at least 5 months. The ENSO strengths are categorized as weak, moderate, and strong when the maximum of *r* belongs to (0.5, 1), (1, 2), (2, +∞), respectively. *La Niña* months are defined by 0° < *θ* < 180°, and *El Niño* months are defined by 180° < *θ* < 360°, where *θ* represents the ENSO phase angle. Author's calculation following Zhang *et al.* (2019).

anomalies within a continuous two-dimensional framework.<fnref n="1" /> Drawing on insights from the literature (Trenberth and Stepaniak, 2001; Ashok et al., 2007; Li et al., 2010), the UECI provides a more refined lens through which to examine ENSO complexity. It consists of two components: the magnitude (*r*), which measures the overall intensity of the ENSO event, and the phase angle (*θ*), which provides a continuous measure—expressed in polar coordinates—reflecting the horizontal spatial distribution of ENSO signals and thus associated *El Niño* and *La Niña* phases (Figure 1 and 2).

**Heterogeneous local climate effects.** The *El Niño*-Southern Oscillation (ENSO) is a primary driver of local and regional climate variability worldwide, exerting profound influences on weather patterns through its teleconnection effects—statistically linked climate anomalies that span vast distances. These teleconnections play a pivotal role in shaping atmospheric circulation, leading to coherent patterns of temperature and precipitation variability. For example, ENSO phases have been linked to droughts, floods, and extreme weather events in regions far away from the tropical Pacific, underscoring their global climatic significance. Accurate characterization of these effects is essential for understanding the localized impacts of ENSO and their broader socioeconomic implications.

The contiguous U.S. territory is particularly exposed to ENSO variations with strong spatial heterogeneity. *La Niña* events, characterized by cooling in the equatorial Pacific, have been linked to distinct climatic anomalies, such as wintertime precipitation deficits over the southern United States (Okumura et al., 2017). Furthermore, persistent *La Niña* episodes exhibit intensified warming signals during spring and summer over Western North America and the Midwest (Jong et al., 2020), underscoring the diverse temporal and spatial impacts of ENSO variability.

Despite ENSO's central role in global climate variability, most economic studies have overlooked the spatial and nonlinear complexity of its teleconnection patterns (Callahan and Mankin, 2023). Empirical analyses commonly rely on simple linear indices of ENSO activity and focus predominantly on the negative impacts of *El Niño*, often treating *La Niña* as a symmetrical but less consequential counterpart. For example, De Marco and Limodio (2023) use visual mapping tools to approximate local ENSO exposure—an impor-

<footnote n="1">¹See Section A1 in the Appendix for a detailed overview of the UECI methodology.</footnote>
tant first step—but their approach abstracts from the regional heterogeneity and temporal persistence of ENSO-induced climate anomalies. This simplification risks attenuating results, particularly by downplaying the distinct and potentially destabilizing effects of *La Niña*, which is treated as a placebo in their design.

In contrast, recent advances in climate science have emphasized the localized and asymmetric nature of ENSO teleconnections. High-resolution methodologies developed by Callahan and Mankin (2023) and Liu et al. (2023) offer improved tools to estimate teleconnection patterns globally and over time. Applying these techniques to the U.S. context (see Appendix A1 and Figure A4), our estimates reveal a pronounced warming signal associated with *La Niña* phases and confirm the asymmetric climate effects of ENSO across the North American domain. These findings challenge the notion of mirrored phase effects and underscore the importance of empirical frameworks that account for the nonlinear, spatially heterogeneous structure of ENSO-driven shocks (e.g. Generoso et al., 2020).

In this study, we adopt methodologies developed by Callahan and Mankin (2023) and Liu et al. (2023) to estimate ENSO's localized meteorological effects across the contiguous United States. We use ERA5-Land data on surface temperature and precipitation, spanning monthly observations from 1950 to the present, to calculate teleconnection patterns. Our approach leverages a nonlinear regression framework that incorporates the UECI's components—the magnitude (*r*) and phase angle (*θ*)—to model the spatial and temporal variability of ENSO-driven climate impacts. This framework enables us to capture complex and asymmetric responses in teleconnection patterns that are often overlooked in simpler linear models.<fnref n="2" />

By integrating these high-resolution spatial and temporal datasets with advanced numerical prediction models, we generate detailed mappings of teleconnection patterns at a granular scale, with a spatial resolution of 0.1° × 0.1° (approximately 9km × 9km at the equator). This approach not only addresses the limitations of earlier methods, which frequently rely on linear assumptions or single-region ENSO indices, but also ensures consistent and spatially comprehensive records of ENSO-related anomalies. The resulting estimates provide a robust basis for understanding ENSO's diverse and localized meteorological impacts.

In the U.S. context, ENSO-induced climate shocks primarily manifest through large-scale and persistent temperature anomalies, which are known to have significant economic consequences. By contrast, ENSO-related precipitation anomalies tend to be more volatile, spatially fragmented, and heterogeneous, limiting their aggregate macroeconomic impact. Accordingly, we treat precipitation anomalies as a form of “placebo test” to assess whether the observed effects of ENSO on U.S. banking stability operate through channels other than temperature-induced disruptions.

## 3.2 Bank data

The banking dataset used in this study provides a comprehensive account of financial activity in the U.S. banking system. It includes nearly 800,000 observations from more than 10,000 U.S. banks and 80,000 branches, covering 30 years of quarterly data from 1994Q1 to 2023Q2. The dataset's branch-level granularity is complemented by detailed geolocation efforts, offering a robust foundation for local banking analysis.

Banks are typically organized in a hierarchical structure, with distinct levels dedicated to strategic, administrative, and operational functions. At the top are the headquarters, which serve as the strategic core, where executives and senior management oversee planning, policy formulation, and governance activities. Headquarters often shape the geographic expansion of the bank, as proximity to them can enhance operational oversight and customer engagement—particularly in dense markets where managerial costs tend to be higher (Oberfield et al., 2024). Beneath the headquarters are the main offices, which operate as key regional hubs. These facilities offer a wide range of financial services, including account management, loan origination, and investment facilitation, and often include specialized units for wealth management, corporate banking, or commercial lending. At the base of the hierarchy are the branches, which act as the primary customer-facing units. Widely distributed across regions, branches are in charge of daily banking operations such as

<footnote n="2">²See Section A1.2 in the Appendix for a detailed presentation of the methodology.</footnote>
processing transactions, managing accounts, and delivering customer service. As emphasized by Oberfield et al. (2024), branch location decisions are shaped by both geographic and economic considerations, as banks aim to balance proximity to retail deposits with regional credit demand.

Our empirical setup exposes banks to climate variability that is spatially transmitted across regions. This feature of our investigation requires two complementary layers of data on the banking sector. First, we use consolidated bank-level information to examine the financial stability risks posed by ENSO-induced climate shocks. Second, we assess how geographically dispersed each bank’s operations are across the U.S. territory. Accordingly, the bank data used in this paper are drawn from two primary sources. Each bank is identified by a unique regulatory identifier provided by the National Information Center (NIC), which allows for consistent tracking across both datasets.

**Bank consolidated domestic activity.** In the United States, *Call Reports* are regulatory financial statements that banks must submit at the headquarter level to federal banking authorities. These reports are a key component of the supervision and examination framework overseen by agencies such as the Federal Deposit Insurance Corporation (FDIC), the Federal Reserve System, and the Office of the Comptroller of the Currency (OCC). All banks are required to file a *Call Report* as of the close of business on the last day of each calendar quarter.

Accordingly, the *Call Reports* provide quarterly detailed information on banks’ balance sheets and income statements from 1992 to 2023. Although historical data are available as far back as 1976 through the *Report of Conditions and Income*, inconsistencies in variable definitions before 1992 limit longitudinal comparability. Moreover, our empirical strategy requires merging these data with branch-level information, which is only available from 1994 onward. The *Call Reports* are consolidated at the bank level and provide a comprehensive view of each banking organization, encompassing not only the main office but also all affiliated branches. The data are compiled from several sources over time, including the Federal Financial Institutions Examination Council (FFIEC), Wharton Research Data Services (WRDS), and the Federal Reserve Bank of Chicago.

The *Call Reports* have undergone periodic revisions, requiring careful reconciliation of variables across different reporting forms to maintain consistency over time. For example, large banks filing the FFIEC 031 form, which is designated for banks with both domestic and foreign offices, report domestic assets indirectly from 2011. Unlike smaller banks that use the FFIEC 041 form and report domestic assets directly, the FFIEC 031 form requires reconstruction of domestic assets using data from Schedule RC-H. This meticulous reconstruction ensures consistency in financial series despite changes in reporting structures, providing a reliable foundation for our analysis of climate-related risks.<fnref n="3" />

**Bank branch local activity.** To fully capture the financial transmission of geographically specific climate shocks, it is essential to extend our analysis to the branch level. While many banks operate as single-branch entities serving specific communities, others maintain extensive networks spanning multiple regions. These networks reflect strategic decisions to expand operations based on local economic opportunities. Understanding the geographic dispersion of banking activities is critical for assessing the impact of localized climate events on financial stability.

A region-specific climate event affects banks only to the extent that they have business operations in the affected area. Macro-analyses that encompass entire national banking sectors often overlook this issue (Klomp, 2014). However, for studies focusing on individual banks, the geographic distribution of branches becomes paramount. Localized shocks cannot be meaningfully aggregated across regions without accounting for the dispersion of banking operations.

Our dataset provides two crucial pieces of information to address these challenges. First, it includes precise branch addresses, from which we obtained accurate latitude and longitude coordinates (see Figure 3), enabling granular identification of where banks operate. Second, it provides detailed deposit amounts for each branch, offering a meaningful proxy for banks’ local activity. Deposits present a broader and more

<footnote n="3">³See Table A2 in the Appendix for more details.</footnote>
<figure><img src="image_6.png" /></figure>

<figure><img src="image_7.png" /></figure>

<figure><img src="image_8.png" /><figcaption>**Figure 3:** U.S. Banking sector from 1994 to 2023 (panel A & B) and its geographical network in 2023 (panel C).</figcaption></figure>

Note: Spatial distribution of bank branches and headquarters based on FDIC Summary of Deposits data. Branch locations are geocoded using a three-step process incorporating OpenStreetMap, U.S. Census, and ZIP code-level approximations to address missing geographic coordinates. Headquarters locations are sourced from Call Reports and mapped according to official registry data.

comprehensive measure of banking activity than commonly used metrics like mortgage origination, which focus narrowly on a specific loan segment. By leveraging deposit data, our study provides a more inclusive assessment of local banking activity and its exposure to geographically specific shocks, offering insights beyond those captured in previous work (e.g., Cortés and Strahan, 2017; Koetter et al., 2020; Rehbein and Ongena, 2022; De Marco and Limodio, 2023).

**Geocoding.** Accurate geolocation of bank branches is essential for linking financial data to localized economic and environmental factors, yet significant gaps exist in the geographic information provided by the FDIC dataset. While data on branch deposits were readily available, precise latitude and longitude information was missing for a substantial proportion of branches. Up to 50% of branches lacked geolocation data in the 1990s, leaving these deposits effectively unaccounted for in regional analyses. These issues persisted into the early 2010s, complicating efforts to link localized banking activity with climate information accurately.

To address this limitation, we developed a systematic three-layer geocoding methodology. First, full address queries were run using the OpenStreetMap (OSM) API. For unmatched cases, we refined results using the U.S. Census API. Finally, residual records were geocoded at the ZIP code level using OSM. To our knowledge, this is the first comprehensive effort to address geolocation gaps in the FDIC dataset. As illustrated in Figure 3, this process resulted in complete geolocation of branch-level deposits, substantially improving the dataset's reliability and enabling precise linkage of localized bank activity with local climate information.

**Bank universe.** Our analysis focuses on the domestic activities of financial institutions chartered as commercial banks within the contiguous United States (CONUS). Restricting the sample to a homogeneous class of banks allows for a more straightforward interpretation of transmission mechanisms and results. Commercial banks play a central role in the U.S. financial system, offering critical services such as credit intermediation, deposit-taking, and liquidity provision. In contrast, other institutions, such as savings banks and credit unions, operate under different regulatory frameworks and narrower mandates, making them less comparable to commercial banks.

We include all banks that operated during the sample period, regardless of whether they failed. Limiting the sample to surviving banks over a 30-year period would overlook the sector's structural evolution, including the trend toward consolidation and the increasing dominance of larger institutions. Moreover, excluding
<table><caption>Table 1: Statistics by bank size</caption><thead><tr><th>1994Q1</th><th>Small banks<br>(&lt;1bn)</th><th>Large banks<br>(1-10bn)</th><th>Large banks<br>(10-25bn)</th><th>Large banks<br>(≥ 25bn)</th><th>All banks</th></tr></thead><tbody><tr><td>Number of banks</td><td>10,208<br>(96.6%)</td><td>315<br>(3.0%)</td><td>33<br>(0.3%)</td><td>16<br>(0.2%)</td><td>10,572<br>(100.0%)</td></tr><tr><td>Number of branches</td><td>31,958</td><td>18,594</td><td>6,531</td><td>5,874</td><td>62,957</td></tr><tr><td>Total assets ($bn)</td><td>982</td><td>1,059</td><td>527</td><td>720</td><td>3,291</td></tr><tr><td>Total deposits ($bn)</td><td>840</td><td>745</td><td>336</td><td>423</td><td>2,345</td></tr><tr><td>Average no. states (σ)</td><td>1.0 (0.1)</td><td>1.0 (0.2)</td><td>1.1 (0.3)</td><td>1.4 (0.8)</td><td>1.0 (0.1)</td></tr><tr><td>Average no. counties (σ)</td><td>1.4 (1.0)</td><td>10.0 (11.5)</td><td>25.7 (18.7)</td><td>29.9 (18.6)</td><td>1.8 (3.4)</td></tr></tbody><thead><tr><th>2023Q1</th><th>Small banks<br>(&lt;1bn)</th><th>Large banks<br>(1-10bn)</th><th>Large banks<br>(10-25bn)</th><th>Large banks<br>(≥ 25bn)</th><th>All banks</th></tr></thead><tbody><tr><td>Number of banks</td><td>3,102<br>(80.4%)</td><td>633<br>(16.4%)</td><td>51<br>(1.3%)</td><td>70<br>(1.8%)</td><td>3,856<br>(100.0%)</td></tr><tr><td>Number of branches</td><td>13,449</td><td>14,437</td><td>3,941</td><td>36,505</td><td>68,332</td></tr><tr><td>Total assets ($bn)</td><td>949</td><td>1,722</td><td>789</td><td>14,008</td><td>17,469</td></tr><tr><td>Total deposits ($bn)</td><td>822</td><td>1,440</td><td>641</td><td>11,957</td><td>14,861</td></tr><tr><td>Average no. states (σ)</td><td>1.1 (0.5)</td><td>1.8 (1.4)</td><td>3.3 (2.3)</td><td>9.2 (9.6)</td><td>1.4 (1.9)</td></tr><tr><td>Average no. counties (σ)</td><td>2.4 (1.9)</td><td>8.8 (9.8)</td><td>22.7 (17.5)</td><td>49.7 (44.8)</td><td>4.6 (10.3)</td></tr></tbody></table>

Note: This table presents summary statistics on U.S. banks by size at the beginning (1994Q1) and end (2023Q1) of the sample period. Banks are classified based on total asset thresholds, with small banks holding less than $1 billion in assets and large banks segmented into size groups. Authors' calculations based on Call Reports and Summary of Deposits data from the Federal Deposit Insurance Corporation (FDIC). All variables are winsorized at the 0.1%-99.9% level to mitigate the influence of extreme values.

failed banks would misrepresent the composition of the banking sector, which includes a substantial proportion of small, single-branch banks, and introduce survival bias, distorting the dynamics of financial stability. Nevertheless, to address mergers, which often involve surviving banks absorbing smaller or failing institutions, we trace these events using the Uniform Bank Performance Report (UBPR) definition employed for supervisory purposes. A significant merger is defined as one resulting in a 25% or greater increase in assets. By incorporating this information, our analysis captures the evolving structure and continuity of banking operations over the sample period.

**Stylized facts on banking sector concentration.** Beyond the reduction in the number of banks, the evolution of the U.S. banking sector between 1994 and 2023 also underscores a pronounced increase in concentration, as illustrated in Figure 3 and Table 1. In the early 1990s, the landscape was characterized by a multitude of small, local banks. Three decades later, there is a substantial decline in the total number of banks, while large institutions now dominate the market, holding a growing share of both assets and deposits. Moreover, these large banks have extended their reach across a wider array of counties, reflecting a more geographically integrated credit and deposit market.

The right panel of Figure 3, which maps the spatial distribution of branches and main offices in 2023, highlights this geographic expansion: the network of branches now spans both urban centers and more peripheral regions. This trend towards concentration raises important questions regarding the system's resilience in the face of localized environmental shocks, such as extreme weather events, and the implications for financial stability and community-level climate adaptation. Recent evidence points to the fact that the spatial expansion of banks and the strategic reallocation of financial resources can significantly influence local economic outcomes and their vulnerability to environmental hazards (see, for example, Oberfield et al., 2024; Rajan and Ramcharan, 2023).
<figure><img src="image_9.png" /><figcaption>**Figure 4:** Diagram of local climate exposure identification</figcaption></figure>

Note: This figure illustrates the three-layered data structure used in the identification strategy. It links the global ENSO climate signal to localized teleconnection exposure at the branch level, which is then aggregated to assess the impact on consolidated bank-level outcomes.

# 4 Identification Strategy

**Exogenous climate shocks.** ENSO events serve as quasi-natural experiments due to their exogenous and quasi-periodic nature, making them an ideal framework for studying the impacts of climate shocks on bank stability. ENSO events, such as *El Niño* and *La Niña* phases, are driven by complex ocean-atmosphere dynamics that are largely independent of local or national economic conditions, significantly reducing the risks of endogeneity in our analysis. Although ENSO cycles occur with an average oscillation period of two to seven years and can be modeled through sophisticated climatic or physical models, the precise timing and intensity of specific ENSO events remain highly unpredictable.<fnref n="4" /> This unpredictability allows ENSO events to be treated as exogenous shocks resulting from quasi-random trials, providing a robust foundation for causal inference.

Despite the exogeneity of ENSO, confounding factors such as global financial crises or region-specific shocks, could influence banking outcomes contemporaneously (Damette et al., 2024, 2026). To address these potential issues, we carefully account for such confounding factors through the inclusion of selected control variables (e.g., macroeconomic conditions and financial indicators) and robustness exercises. These robustness checks include alternative model specifications, different classifications of ENSO event intensities, and exclusion of periods coinciding with significant financial disruptions (see Section 8).

Our identification strategy leverages a three-layered data architecture to causally link ENSO-induced climate shocks to U.S. banking outcomes. First, we treat ENSO as an exogenous global climate phenomenon, using the Unified ENSO Complex Index (UECI) to capture its phase-specific intensity and temporal variation. Second, we translate these global anomalies into localized weather deviations by constructing grid-level teleconnection indices—primarily for temperature—which are assigned to bank branches based on their geocoded locations and then aggregated at the institution level using deposit-weighted averages. Third, we match these exposure measures with quarterly bank-level financial data from FFIEC Call Reports, enabling us to assess the impact of ENSO shocks on balance sheet health and performance. Figure 4 illustrates the integration of these three layers, from global climate signals to local bank responses.

**Local projections.** We use Local Projections (LPs) as introduced by Jordà (2005), a flexible and robust econometric methodology for estimating the dynamic effects of ENSO shocks on bank stability. LPs involve a sequence of regressions of the endogenous variable, shifted forward in time, onto its lags and a set of

<footnote n="4">⁴For a detailed discussion of ENSO dynamics and their conceptual underpinnings, see Timmermann et al. (2018), Roberts et al. (2016), and Jin (1997).</footnote>
control variables, directly estimating the dynamic response over multiple horizons. Compared to traditional Vector AutoRegressive (VAR) models, LPs impose fewer parametric restrictions, enabling the computation of Impulse Response Functions (IRFs) without assuming a fixed structure for the data-generating process.

LPs offer significant advantages for this analysis. Recent studies show that LP and VAR models yield comparable impulse response estimates, but LP inference is simpler and more robust to specification errors (Montiel Olea and Plagborg-Møller, 2021; Plagborg-Møller and Wolf, 2021; Jordà, 2023; Olea et al., 2025). Additionally, LPs are less prone to the “curse of dimensionality,” which can affect VAR models when dealing with numerous variables or lags. These features make LPs particularly well-suited for analyzing the non-linear and time-varying impacts of ENSO shocks, which differ across regions and banking institutions.

This approach enables us to quantify both short- and medium-term effects, capturing immediate responses and the persistence of climate-induced banking instability. By incorporating fixed effects and exploiting geolocated branch-level data, LPs allow us to link regional ENSO-induced climate anomalies to localized banking activity, ensuring the analysis reflects heterogeneity across banks and regions.

Define $y_{i,t}^k$ as the various ($k = 1, ..., K$) bank stability metrics (e.g., Z-score) for bank $i = 1, ..., N$ at time $t$. The $h$-period ahead cumulative change in bank stability, $\Delta_h y_{i,t+h}^k \equiv y_{i,t+h}^k - y_{i,t}^k$ is the long-difference or percentage point change in the metric over $h$ steps ($h = 0, ..., H$).<fnref n="5" /> Let $r_{i,t}^\theta$ represent the magnitude of the ENSO-induced climate shock for ENSO phase $\theta$ (El Niño or La Niña), affecting bank $i$ at time $t$. A set of bank-level and regional controls are consolidated into the vector $X_{i,t}$. This design measures how deviations in $r_{i,t}^\theta$ from its baseline level $\tilde{r}_{i,t}^\theta$ influence bank stability. The cumulative response (CR) of bank stability as the climate shock $r_{i,t}^\theta$ deviates by $\delta$ from a baseline level $\tilde{r}_{i,t}^\theta$ is estimated as:

$$CR(\Delta_h y_{i,t+h}^k, \delta) = E(\Delta_h y_{i,t+h}^k | r_{i,t}^\theta = \tilde{r}_{i,t}^\theta + \delta; X_{i,t}) - E(\Delta_h y_{i,t+h}^k | r_{i,t}^\theta = \tilde{r}_{i,t}^\theta; X_{i,t}). \quad (1)$$

Assuming linearity, this cumulative response is the sum of impulse responses (IR) over the horizon $h$: $CR(\Delta_h y_{i,t+h}^k, \delta) = \sum_{i=1}^h IR(\Delta y_{i,t+i}^k, \delta)$. To avoid relying on linearity, Jordà (2023) suggests to estimate the cumulative response using fixed-effects panel regressions, which leads to the following generic specification that will serve as basis for different exercises:

$$\Delta_h y_{i,t+h}^k = \lambda_h^k y_{i,t-1}^k + \omega_h^k r_{i,t}^\theta + \Gamma_h^k X_{i,t} + \phi_{h,i}^k + q_{h,t}^k + u_{h,i,t}^k, \quad (2)$$

where $\lambda_h^k$ captures the autoregressive component of the dependent variable $y_{i,t-1}^k$, $\omega_h^k$ measures the effects of ENSO shocks, and $\Gamma_h^k$ represents the coefficients for the set of control variables $X_{i,t}$. Fixed effects at the bank level ($\phi_{h,i}^k$) and quarter fixed effects ($q_{h,t}^k$) account for unobserved heterogeneity, while $u_{h,i,t}^k$ captures the residual error. To ensure valid statistical inference, we use robust standard errors clustered at the bank level, effectively addressing intra-group dependence and preventing bias from within-bank autocorrelation.

Our specification prioritizes parsimony, relying on the exogenous nature of ENSO shocks to ensure clean identification by avoiding the inclusion of “bad regressors” that may introduce noise and undermine causal inference (Angrist and Pischke, 2009; Brei et al., 2024). To account for potential confounding factors, we incorporate subprime-era time fixed effects based on NBER recession dating, capturing the disruptive impact of the financial crisis.<fnref n="6" /> Bank-specific fixed effects are included to control for time-invariant heterogeneity across institutions. Similarly, merger controls account for structural changes in the banking sector over time. Bank financial data is winsorized at the 0.1%-99.9% level to mitigate the influence of extreme values.

Additionally, we address seasonality in bank accounting by incorporating quarter fixed effects and transforming dependent variables using a 4-quarter uncentered moving average, a standard conservative practice in

<footnote n="5">⁵Using long-difference specifications in LPs offers important advantages in terms of bias reduction. Specifically, the small-sample bias of order $O(T^{-1})$ identified by Herbst and Johannsen (2024) is largely suppressed when long-differences are used instead of levels. This is particularly important in dynamic settings with high persistence, where traditional estimators suffer from significant bias that compounds over time. Long-differencing mitigates this issue by breaking the correlation between regressors and residuals, thus yielding more reliable impulse response estimates even in relatively short samples.</footnote>

<footnote n="6">⁶See Section 8 for further investigation of the robustness of the results.</footnote>
time-series econometrics. Beyond mitigating seasonal fluctuations, this smoothing procedure also helps address the inherent noisiness of LP estimates, which do not impose cross-horizon smoothness restrictions—as VARs typically do. This feature of LPs contributes to their lower bias, but comes at the cost of more volatile estimates, reflecting the standard bias-efficiency trade-off (Li et al., 2024). To alleviate this, as suggested by Jordà and Taylor (2025), smoothness can be restored through simple techniques such as rolling-window averages, which act as nonparametric kernel estimators of the conditional mean and help recover the underlying response profile from small-sample variation.

In our setting, difference-in-differences (DiD) designs are misaligned with the nature of climate variability we study due to several identification challenges. First, ENSO-induced climate anomalies affect the vast majority of the U.S., often with opposite-signed effects across regions. This spatial pervasiveness and heterogeneity undermine the credibility of untreated control groups and make the parallel trends assumption untestable. Second, ENSO is a recurring and non-absorbing phenomenon, alternating between *El Niño*, *La Niña*, and neutral phases. This rules out the possibility of defining stable “never-treated” or “not-yet-treated” groups, which are required for staggered DiD designs. Third, any attempt to binarize ENSO exposure— whether by setting thresholds on ocean temperatures, duration, or geographic footprint—relies on arbitrary cutoffs and introduces measurement discretion. Moreover, the spatial correlation of ENSO shocks violates the no-interference assumption central to DiD estimation. In light of these limitations, we adopt a dynamic panel framework based on local projections (LP), which flexibly estimates cumulative and horizon-specific responses to continuous climate shocks. This method accommodates nonlinearity, phase asymmetries, and spatial heterogeneity in exposure, while exploiting both the temporal and geographic variation in ENSO. It also aligns with a growing literature on the macroeconomic effects of ENSO that relies on dynamic panel methods rather than static, binary-treatment designs.

**Dependent variables and financial transmission channels.** Our primary dependent variable is the widely recognized bank Z-score, a standard measure of financial stability. The Z-score is defined as the sum of a bank's return on assets (ROA) and equity-to-assets ratio (EQ), standardized by the standard deviation of ROA (see Laeven and Levine, 2009; Dafermos et al., 2018). This metric compares a bank's resilience, as reflected in its returns and equity, with the volatility of those returns, providing an indicator of the distance-to-default. Specifically, it represents the number of standard deviations a bank's returns need to drop to exhaust its capital, making it a measure of insolvency likelihood (Hannan and Hanweck, 1988). A higher Z-score indicates greater financial stability, while a lower Z-score reflects heightened fragility. Formally, it is calculated as:

$$z_{i,t} = \frac{\text{ROA}_{i,t} + \text{EQ}_{i,t}}{\sigma_{i,t}^{\text{ROA}}}, \quad (3)$$

where $\sigma_{i,t}^{\text{ROA}}$ is the standard deviation of ROA computed over a backward looking rolling window of eight quarters. This approach captures recent volatility in bank performance while smoothing out short-term fluctuations (Noth and Schüwer, 2023). To ensure comprehensive data coverage, we extend Call Reports data for ROA back to 1992, allowing full availability of all variables starting from 1994.

To refine our analysis and investigate the channels through which financial stability is affected, we decompose the Z-score into profitability and risk metrics. Climate shocks may impair borrowers' ability to service debt, leading to an increase in non-performing loans (NPLs) and foreclosures. The NPL ratio, calculated as the sum of loans 90+ days past due and non-accrual loans divided by total assets, serves as a key indicator of loan book quality. Similarly, the foreclosure ratio (FOR), which measures foreclosed properties relative to total assets, highlights the burden of problematic loans on a bank's balance sheet. Rising NPLs and foreclosures erode capital ratios, impair profitability, and threaten solvency.

The cumulative effects of credit risks directly impact profitability. Metrics such as Return on Assets (ROA) evaluate financial performance, while the cost-to-income ratio (CIR) measures operational efficiency by comparing expenses to revenue. Declines in these profitability metrics indicate stress on a bank's financial health. Summary statistics are presented in Table A1 in the Appendix.
# 5 ENSO Magnitude and Banking Stability: Baseline estimates

In this section, we examine the baseline effects of ENSO magnitude on banking stability by employing continuous shock variables $r_t^{niño}$ and $r_t^{niña}$, which allow for a flexible, non-linear framework. This approach moves beyond the simplifying assumption that *La Niña* is merely the mirror image of *El Niño*, enabling us to capture their potentially asymmetric impacts on financial stability (Callahan and Mankin, 2023). The baseline specification utilizes the global UECI metric, focusing solely on ENSO phase and intensity while abstracting from region-specific exposure or teleconnection effects. By concentrating on the aggregate shock, this method provides a clean and comprehensive measure of ENSO's global influence on banks' stability. To estimate these effects, we use the following Local Projection (LP) model:

$$ \Delta_h y_{i,t+h}^k = \lambda_h^k y_{i,t-1}^k + \omega_h^k r_t^{niño} + \alpha_h^k r_t^{niña} + \Gamma_h^k X_{i,t} + \phi_{h,i}^k + q_{h,t}^k + u_{h,i,t}^k \quad (4) $$

Here, $y_{i,t}^k$ denotes various banking outcomes, such as stability, performance, or credit risk measures. The lagged dependent variable, $\lambda_h^k y_{i,t-1}^k$, captures persistence in banking outcomes, reflecting the inertia inherent in financial conditions. Bank-level fixed effects, $\phi_{h,i}^k$, control for time-invariant heterogeneity across institutions, while quarter fixed effects, $q_{h,t}^k$, account for seasonal and macroeconomic shocks. The analysis focuses on a three-year window ($H = 12$) following the onset of an ENSO event, a time horizon frequently adopted in the literature to capture the persistence of climate-related and economic effects. For example, Liu et al. (2023) demonstrate that ENSO-induced impacts on environmental and economic outcomes often endure for up to three years, making this timeframe particularly suitable for evaluating cumulative effects on banking stability. To illustrate the dynamics, we plot Impulse Response Functions (IRFs) for a strong ENSO shock level of $r_t^\theta = 2$, a widely recognized threshold in the literature. This choice remains conservative, as ENSO magnitudes can reach nearly double this level in extreme cases. By integrating both the magnitude ($r$) and phase ($\theta$) of ENSO events, this framework offers a robust and insightful basis for understanding their medium-term effects on financial outcomes $y_{i,t}^k$.

The results are reported in Figure 5. The analysis reveals that *La Niña* events exhibit magnitudes approximately twice as high as *El Niño* events, reflecting their distinct characteristics in influencing U.S. climatic and economic conditions. This asymmetry aligns with prior literature emphasizing the differing impacts of ENSO phases. Studies such as Smith and Katz (2013) and Hoerling et al. (2013) highlight that in the U.S. *La Niña* is more frequently associated with persistent droughts, extreme heat waves, and broader disruptions across multiple sectors. While agriculture remains a well-documented channel of impact, sectors such as real estate, manufacturing, and energy also face significant consequences. Droughts, for instance, can disrupt energy production in hydropower-dependent regions, while extreme weather events damage infrastructure and delay industrial output. These findings underscore the critical importance of moving beyond traditional frameworks that treat *El Niño* and *La Niña* as mirror images and instead accounting for phase-specific heterogeneity when evaluating the macroeconomic and financial consequences of ENSO phenomena.

The sequence of effects highlights how ENSO shocks destabilize banks. A *La Niña* shock with a magnitude of $r_t^\theta = 2$, our baseline strong shock, leads to a 15% cumulative decline in Z-scores, signaling a substantial drop in banks' stability. This decline is driven by losses in profitability (ROA), operational efficiency (CIR), and solvency (EQ). Specifically, the -0.03 percentage point ($pp$) reduction in ROA corresponds to an average -15% drop in profitability, while the -0.4$pp$ decline in EQ reflects a -3.6% erosion in equity-to-asset ratios. Concurrently, CIR increases by approximately 2.2 percentage points, representing a 3% rise in operating costs relative to income. Together, these findings underscore the cumulative stress ENSO-induced shocks impose on banks' financial health.

Credit risk plays a central role in amplifying the disruptive effects of ENSO shocks. The increase in non-performing loans (NPLs) and foreclosure rates (FOR) highlights the strain on borrowers' repayment capacity. A *La Niña* shock results in a 0.13$pp$ rise in NPLs, representing an average 17% increase, while the same magnitude shock leads to a 0.13$pp$ increase in FOR, corresponding to a 40% rise. These dynamics reflect how climate-induced disruptions can cascade through the banking system, with deteriorating loan quality
<figure><img src="image_10.png" /><figcaption>Figure 5: Baseline estimates, continuous *El Niño* and *La Niña* shocks ($r = 2$)</figcaption></figure>

Note: Impulse Response Functions (IRFs) based on equation (4) estimated using the Local Projections (LP) methodology (Jordà, 2005, 2023; Jordà and Taylor, 2025). Shaded areas denote 90% confidence intervals, with robust standard errors clustered at the bank level to account for intra-group dependence and ensure valid inference. IRFs report cumulative changes expressed in percentage points (pp), except for the Z-score, which is presented as cumulative percentage changes (%).

placing significant pressure on balance sheets.

The scale of these shocks prompts a key question: how do they compare with the stress levels observed during major systemic crises? To contextualize their economic significance, we benchmark the estimated effects against those experienced during the 2008 subprime crisis and the COVID-19 shock. For instance, our *La Niña* estimate of a 0.13*pp* increase in foreclosure rates corresponds to a 40% rise relative to pre-shock averages. While this is substantial in proportional terms, it remains modest compared to the peaks exceeding 2*pp* seen during the Great Recession. Nevertheless, this increase is more pronounced than the early COVID-19 period, when widespread moratoria and fiscal interventions curtailed foreclosure activity.

Similarly, the 0.13*pp* rise in non-performing loans (NPLs) reflects a 17% increase from average baseline levels. Yet in absolute terms, the resulting NPL ratios remain well below the 5*pp* highs reached in 2009. The decline in return on assets (ROA), estimated at -0.03*pp* (or roughly -15%), mirrors the profitability losses observed in the initial quarters of the COVID-19 crisis, driven by liquidity stress and elevated provisioning. In contrast, the equity ratio declines only slightly—by -0.03*pp*, or around -0.4%—suggesting that capital buffers remain largely intact. Taken together, these figures indicate that while ENSO shocks do not trigger financial disruptions on the scale of global crises, they nonetheless represent a meaningful source of medium-term strain, particularly through deteriorating credit quality and reduced profitability.

The timing of these effects aligns with the expected dynamics of banking responses to economic shocks. Income-related impacts, such as declines in ROA and increases in CIR, materialize quickly, peaking around seven quarters after the initial shock. Credit risk indicators, such as NPLs, exhibit natural latency due to their 90-day classification, while foreclosures peak at approximately 11 quarters, reflecting the time required for non-performing loans to transition into foreclosure. These findings align with the sequential dynamics of banking responses to economic shocks, highlighting how disruptions ripple through, heightening credit risk, loss in performance and resilience metrics. Overall, these effects illustrate the persistence of ENSO-induced
shocks, with most banks recovering after two and a half years.

ENSO-induced impacts are strongly tied to the intensity of the climatic shock. While the baseline analysis focuses on a strong shock level ($r_t^θ = 2$), larger shocks elicit correspondingly greater effects. For instance, the maximum observed magnitudes of *El Niño* ($r_t^{\text{niño}} \approx 5$) and *La Niña* ($r_t^{\text{niña}} \approx 3$) imply significantly heightened disruptions, with larger deviations compounding stress across banking outcomes. These findings highlight the critical need to account for the localized nature of climate exposures, which can vary substantially across regions and banking institutions, prompting a closer examination of teleconnection-based measures.

While ENSO shocks significantly impact banking stability through credit risk and profitability channels, our analysis finds no evidence of a liquidity risk channel.<fnref n="7" /> Specifically, the deposits-to-assets ratio remains largely unaffected, suggesting that banks do not experience substantial funding pressures following climate-induced disruptions. This finding is consistent with prior research indicating that, in developed financial systems, deposit withdrawals after natural disasters tend to be limited due to the stability of retail deposits and the presence of deposit insurance schemes (Brei et al., 2019; Chabot and Bertrand, 2023).

# 6 Local Climate Exposure

**Teleconnection-based shocks.** ENSO's global climate variability is driven by teleconnections: statistically significant climate anomalies spanning thousands of kilometers. In this study, we use high-resolution temperature and precipitation data from the ERA-Land database at a 0.1° spatial resolution to delineate these teleconnection patterns. Using state-of-the-art methods proposed by Callahan and Mankin (2023) and Liu et al. (2023), we estimate nonlinear teleconnection indices for both *La Niña* and *El Niño* events, providing a comprehensive evaluation of ENSO's asymmetric influence across the contiguous United States.<fnref n="8" />

We construct two sets of teleconnection indices for each grid cell and ENSO phase $θ$: one for temperature-related influences, denoted $τ^θ$, and another for precipitation-related influences, denoted $ρ^θ$. These indices reflect the heterogeneous impacts of ENSO on local weather by incorporating two layers of non-linearity: the asymmetric effects of *El Niño* and *La Niña*, and the directional (positive vs. negative) component of weather anomalies associated with fluctuations in the ENSO signal. Figure 6 illustrates the spatial distribution of ENSO-induced temperature anomalies as captured by $τ^θ$, showing that *La Niña* is associated with stronger and more widespread teleconnections than *El Niño*. This confirms that *La Niña* is not a simple mirror image of *El Niño*, highlighting important nonlinearities between the two phases and corroborating recent evidence from the climate science literature (Callahan and Mankin, 2023; Liu et al., 2023).

In the main analysis, we use these temperature-based indices as the primary metric to evaluate the transmission of ENSO shocks to banking stability, while precipitation-based indices $ρ^θ$ are explored as a placebo test in Section 8. Given that ENSO-related precipitation anomalies in the U.S. are much more volatile, spatially fragmented, and economically less consequential than temperature patterns, we treat precipitation-based teleconnections as a form of falsification test. The underlying idea is that if our identification strategy is valid and the estimated effects are genuinely driven by economically meaningful climate channels, then the influence of ENSO shocks on bank outcomes should be significantly weaker—or entirely absent—when using precipitation-based exposure instead of temperature-based exposure. In this context, a null or muted effect for $ρ^θ$ helps to rule out spurious correlations and strengthens the case for a causal interpretation of the results obtained with $τ^θ$.

<footnote n="7">⁷Full estimation results supporting this conclusion are provided in Appendix A4.</footnote>

<footnote n="8">⁸See Section A1.2 in the Appendix for a detailed description of the methodology applied to the United States. We also present our reproduction of the global teleconnection patterns, as calculated by Liu et al. (2023), to illustrate how *La Niña* conditions exert a distinct influence on U.S. climate anomalies compared to those observed worldwide (see Figure A4 panels A and B of Section A1.2 in the Appendix). The teleconnection estimation procedure assumes that the teleconnection patterns remain stable over time. We illustrate the temporal stability of teleconnection patterns by estimating worldwide teleconnection patterns using 42-rolling period windows. See Figure A4 panels C and D of Section A1.2 in the Appendix.</footnote>
<figure><img src="image_11.png" /><figcaption>**Figure 6:** Nonlinear ENSO teleconnections and surface air temperature anomalies across the U.S.
Note: This map illustrates the pixelwise teleconnection indices, denoted as $\tau_{x,y}^{\text{nino}}$ for El Niño events (A) and $\tau_{x,y}^{\text{nila}}$ for La Niña events (B). These indices represent the strength and direction of ENSO's influence on temperature anomalies across the contiguous United States during a typical ENSO season. The values are expressed in units of standard deviation, indicating the degree to which temperatures deviate from their climatological norms during El Niño and La Niña phases. Author's calculation following Callahan and Mankin (2023) and Liu et al. (2023).</figcaption></figure>

**Geographic proximity.** To capture banks' localized exposure to ENSO-induced climate anomalies, we assign teleconnection indices to the geolocations of bank branches. This approach is grounded in robust empirical evidence that local presence remains a key determinant of credit allocation—especially for small businesses (Beck et al., 2018). Historically, the U.S. banking system has featured a dense branch network (Figure 3), fostering close proximity between banks and borrowers. Despite major advances in digital banking, proximity continues to matter: credit access is significantly reduced following branch closures, with effects sharply localized within a six-mile radius (Nguyen, 2019). Such findings underscore that branches serve not only as transaction points but also as hubs of relationship-based lending and information gathering, particularly for information-sensitive borrowers (Degryse and Ongena, 2005; Drexler and Schoar, 2014). In line with Petersen and Rajan (2002) and more recent analyses (Nguyen, 2019; Abecasis and Walker, 2023), firms typically borrow from nearby lenders, with median borrower–lender distances of 4 miles, remaining under 10 miles in most cases today, emphasizing the continued importance of branch-based intermediation.

The persistence of distance effects echoes long-standing concerns about informational asymmetries in credit markets (Akerlof, 1970; Stiglitz and Weiss, 1981), and reinforces the notion that geographic proximity facilitates the transfer of soft information critical to lending decisions. Branch location strategies reflect a balance between access to retail deposits and localized loan demand (Oberfield et al., 2024), consistent with canonical spatial models (Salop, 1979) and the relationship lending literature (Sharpe, 1990; Rajan, 1992). Even as technology drastically widens the reach of arm's length financing in the United States, it has not fully substituted for the informational advantages of in-person banking relationships (Glaeser, 1998).

Methodologically, our approach is supported by the observed spatial patterns of ENSO-induced climate anomalies. As shown in Figure 6, local climate exposure exhibits relative smoothness over subsets of counties, suggesting that banks and their clients within a given region experience similar climatic conditions. This supports the validity of our branch-based exposure measure as a reasonable approximation of a bank's overall climate risk exposure. We aggregate branch-level teleconnection indices $\tau_{i,b}^{\theta}$ into a bank-level measure $\tau_{i,t}^{\theta}$ using a weighted average, where branch deposit shares $\delta_{i,b,t}$ determine relative exposure:

$$ \delta_{i,b,t} = \frac{\text{branch's deposits}_{b,t}}{\text{bank's deposits}_{i,t}} \quad \text{s.t.} \quad \sum_{b} \delta_{i,b,t} = 1. \qquad (5) $$
The resulting bank-level teleconnection index is expressed as:

$$ \tau_{i,t}^{\theta} = \sum_{b} (\tau_{i,b}^{\theta} \times \delta_{i,b,t}). \qquad (6) $$

This approach circumvents the limitations of activity-specific proxies—such as Community Reinvestment Act data for agricultural lending or Home Mortgage Disclosure Act data for mortgage origination—which often provide incomplete coverage due to asset-based reporting thresholds. By instead relying on branch-level deposit shares, the method offers a more comprehensive measure of each bank’s regional footprint and, consequently, its exposure to ENSO-induced teleconnection patterns.

Based on the bank-level teleconnection index, we conduct additional estimations to deepen our understanding of the heterogeneous impacts of ENSO-induced climate shocks on banking stability. Specifically, we implement two complementary exercises that explore distinct dimensions of bank-specific climate exposure using granular temperature teleconnection indices, $\tau_{i,t}^{\theta}$. The first investigates the intensity of exposure, while the second focuses on the directional nature of the anomaly. Together, these exercises provide a more nuanced characterization of ENSO’s disruptive potential, capturing both the magnitude of exposure and the qualitative features of the underlying climate anomalies.

**Exposure intensity.** The first exercise focuses on the intensity of exposure, measured by the absolute value of the teleconnection index, $|\tau_{i,t}^{\theta}|$. By interacting the magnitude of the ENSO phase-specific shock, $r_t^{\theta}$, with the absolute bank-specific teleconnection, this exercise captures the disruption intensity experienced by each bank. The use of the absolute value abstracts from the anomaly’s specific nature—whether it represents a positive or negative deviation—and instead evaluates the overall strength of ENSO-induced disruptions. The corresponding specification is:

$$ \Delta_h y_{i,t+h}^k = \lambda_h^k y_{i,t-k}^k + \omega_h^k (r_t^{\text{niño}} \times |\tau_{i,t}^{\text{niño}}|) + \alpha_h^k (r_t^{\text{niña}} \times |\tau_{i,t}^{\text{niña}}|) \\ + \Gamma_h^k X_{i,t} + \phi_{h,i}^k + q_{h,t}^k + u_{h,i,t}^k \qquad (7) $$

This formulation highlights how global ENSO shocks ($r_t^{\theta}$) interact with localized teleconnection intensities to influence bank stability.

The results, presented in Figures 7 and 8, reveals that the intensity of ENSO-induced disruptions to banking stability is strongly linked to the magnitude of the teleconnection index, $|\tau_{i,t}^{\theta}|$. Higher teleconnection intensities amplify the destabilizing effects of global ENSO shocks ($r_t^{\theta}$), doubling the observed impacts when moving from average to strong teleconnection levels. This finding, robust across both La Niña and El Niño phases, underscores the critical role of localized climate exposure in shaping financial vulnerabilities. The effects are evident in sharp declines in Z-scores for banks located in high-intensity regions, with La Niña and El Niño shocks producing consistent patterns of destabilization.

Temporal patterns further reveal how ENSO shocks propagate through financial systems. Early impacts on income, such as shifts in ROA and CIR, peak around seven quarters. Credit risk indicators, including NPLs and FOR, exhibit delayed responses, with foreclosures reaching their maximum effect at approximately 11 quarters. This sequence reflects the natural latency in credit risk materialization, emphasizing the persistence of ENSO-induced disruptions, which often require over two years for banks to fully recover.

Overall, the findings highlight the disproportionate vulnerability of banks in regions with strong ENSO-driven temperature anomalies. The higher the teleconnection intensity, the greater the destabilization, and this pattern is consistent for both La Niña and El Niño phases. These results stress the importance of addressing localized climate exposure to mitigate physical risks posed by ENSO-induced climatic disruptions.

**Anomaly type (non-linearity).** The second exercise investigates the type of anomaly, distinguishing between positive and negative teleconnections. The teleconnection index is decomposed into two components: $\tau_{i,t}^{\theta+}$, capturing positive deviations, and $\tau_{i,t}^{\theta-}$, capturing negative deviations such that:

$$ \tau_{i,t}^{\theta+} = \begin{cases} \tau_{i,t}^{\theta} & \text{if } \tau_{i,t}^{\theta} > 0 \\ 0 & \text{otherwise} \end{cases} \qquad \tau_{i,t}^{\theta-} = \begin{cases} \tau_{i,t}^{\theta} & \text{if } \tau_{i,t}^{\theta} < 0 \\ 0 & \text{otherwise} \end{cases} \qquad (8) $$
<figure><img src="image_12.png" /><figcaption>Figure 7: *El Niño* shocks by teleconnection intensity to surface air temperature anomalies</figcaption></figure>

Note: Impulse Response Functions (IRFs) based on equation (7) estimated using the Local Projections (LP) methodology (Jordà, 2005, 2023; Jordà and Taylor, 2025). Shaded areas denote 90% confidence intervals, with robust standard errors clustered at the bank level to account for intra-group dependence and ensure valid inference. IRFs report cumulative changes expressed in percentage points (pp), except for the Z-score, which is presented as cumulative percentage changes (%).

<figure><img src="image_13.png" /><figcaption>Figure 8: *La Niña* shocks by teleconnection intensity to surface air temperature anomalies</figcaption></figure>

Note: Impulse Response Functions (IRFs) based on equation (7) estimated using the Local Projections (LP) methodology (Jordà, 2005, 2023; Jordà and Taylor, 2025). Shaded areas denote 90% confidence intervals, with robust standard errors clustered at the bank level to account for intra-group dependence and ensure valid inference. IRFs report cumulative changes expressed in percentage points (pp), except for the Z-score, which is presented as cumulative percentage changes (%).
This disaggregation allows for a detailed analysis of how the direction of the teleconnection anomaly— whether amplifying or mitigating regional climate conditions—affects banking outcomes. The correspond- ing specification is akin to a state-dependent local projection (Gonçalves et al., 2024), where the impulse responses vary according to the sign of the local climate anomaly:

$$
\begin{align}
\Delta_h y_{i,t+h}^k ={}& \lambda_h^k y_{i,t-1}^k + \omega_{1,h}^k (r_t^{niño} \times \tau_{i,t}^{niño-}) + \alpha_{1,h}^k (r_t^{niña} \times \tau_{i,t}^{niña-}) \tag{9} \\
& + \omega_{2,h}^k (r_t^{niño} \times \tau_{i,t}^{niño+}) + \alpha_{2,h}^k (r_t^{niña} \times \tau_{i,t}^{niña+}) \nonumber \\
& + \Gamma_h^k X_{i,t} + \phi_{h,i}^k + q_{h,t}^k + u_{h,i,t}^k \nonumber
\end{align}
$$

By differentiating between positive and negative teleconnections, this exercise offers a more nuanced under-
standing of how distinct types of climate anomalies interact with ENSO shocks to influence bank stability.

The non-linear exercise provides a valuable refinement of the climate transmission mechanism of physical
risks, enabling a deeper understanding of how specific types of climate anomalies—positive and negative—
affect banking stability. By isolating the effects of positive and negative teleconnection indices for both
*El Niño* and *La Niña* phases, the analysis highlights how climatic disruptions propagate through financial
systems. This exercise demonstrates that the type of anomaly significantly influences the magnitude and
direction of financial impacts, confirming the importance of considering non-linear dynamics when assessing
ENSO-induced shocks.

For *El Niño* events, the results clearly indicate that financial disruptions and adverse stability outcomes are primarily driven by positive temperature anomalies. Positive anomalies lead to significant declines in key metrics such as return on assets, equity-to-asset ratios, and the cost-to-income ratio, confirming the role of operational inefficiencies and reduced profitability in amplifying financial instability (Figure 9). The credit risk channel also becomes evident, with the foreclosure ratio increasing significantly, indicating heightened borrower distress and deteriorating loan quality. Non-performing loans, while showing upward trends, display slightly less precise responses to positive anomalies. Negative anomalies, on the other hand, have muted effects, with statistically insignificant impacts across most metrics. Return on assets trends slightly upward under negative anomalies, suggesting minimal disruptions to financial performance. This asymmetry highlights that *El Niño*-related financial instability is predominantly driven by the adverse effects of positive temperature anomalies, with limited disruptions stemming from negative anomalies.

A similar pattern emerges for *La Niña* events, but with significantly amplified financial disruptions. Positive anomalies during *La Niña* generate effects approximately 2.5 times larger than those observed during *El Niño*, a result of *La Niña*'s stronger teleconnection intensities (Figure 10). Positive anomalies lead to pronounced declines in equity-to-asset ratios, reflecting weakened loss-absorption capacity as banks struggle to offset rising losses. This is indicative of reduced buffer mechanisms against adverse conditions, which exacerbates systemic vulnerabilities. The dynamics of non-performing loans reveal a significant increase in credit risk under positive anomalies, highlighting heightened repayment difficulties among borrowers. Interestingly, the foreclosure ratio shows that a smaller proportion of non-performing loans transitions to foreclosure under positive anomalies, suggesting variability in how credit risks materialize depending on the type of anomaly.

Negative anomalies under *La Niña* also exhibit destabilizing effects, though they are less severe than positive anomalies. Equity-to-asset ratios decline under negative anomalies, albeit to a lesser extent, reflecting ongoing stress to banks’ solvency buffers. While the cost-to-income ratio and return on assets respond less strongly to negative anomalies, the increase in non-performing loans remains notable, indicating that credit risk persists even in less severe disruptions. Together, the results demonstrate that *La Niña* disruptions are broader and more intense, with both positive and negative anomalies contributing to financial instability, albeit through different mechanisms.

The severe 2011 Texas drought exemplifies the localized financial distress induced by *La Niña* events. This extreme drought, among the worst in the State’s history, resulted in over $7.62 billion in agricultural losses, triggering widespread loan defaults, particularly in farm-dependent banking regions (Texas Forest
<figure><img src="image_14.png" /><figcaption>Figure 9: *El Niño* shocks by teleconnection direction to surface air temperature anomalies</figcaption></figure>

Note: Impulse Response Functions (IRFs) based on equation (9) estimated using the Local Projections (LP) methodology (Jordà, 2005, 2023; Jordà and Taylor, 2025). Shaded areas denote 90% confidence intervals, with robust standard errors clustered at the bank level to account for intra-group dependence and ensure valid inference. IRFs report cumulative changes expressed in percentage points (pp), except for the Z-score, which is presented as cumulative percentage changes (%).

<figure><img src="image_15.png" /><figcaption>Figure 10: *La Niña* shocks by teleconnection direction to surface air temperature anomalies</figcaption></figure>

Note: Impulse Response Functions (IRFs) based on equation (9) estimated using the Local Projections (LP) methodology (Jordà, 2005, 2023; Jordà and Taylor, 2025). Shaded areas denote 90% confidence intervals, with robust standard errors clustered at the bank level to account for intra-group dependence and ensure valid inference. IRFs report cumulative changes expressed in percentage points (pp), except for the Z-score, which is presented as cumulative percentage changes (%).
Service, 2012). Small and mid-sized banks with high agricultural loan exposure faced sharp increases in non-performing loans as prolonged dry conditions reduced farmers’ repayment capacity. Similarly, the 2020–2023 La Niña event exacerbated wildfire risks in California, leading to direct economic losses and reduced property values in affected areas (Westerling, 2016). Regional banks with concentrated mortgage portfolios in high-risk areas experienced increased foreclosure risks, as declining real estate valuations eroded collateral quality and repayment capacity. These episodes highlight how La Niña-driven climate anomalies propagate financial distress across geographically concentrated sectors.

The results are consistent with the literature documenting localized effects of ENSO-driven climatic disruptions and emphasize the critical role of teleconnection intensity in shaping banking stability outcomes. By highlighting the disproportionate vulnerabilities of banks in regions with strong teleconnection anomalies, this exercise underscores the importance of understanding how climatic shocks propagate via operational inefficiencies, profitability erosion, and solvency deterioration. The observed dynamics of credit risk—ranging from borrower distress to variations in the foreclosure ratio—illustrate the intricate interplay between climatic anomalies and financial system stability. These findings provide a comprehensive and coherent framework for assessing the localized effects of climate-induced disruptions on banking stability.

# 7 Banks’ Resilience Factors

This section investigates heterogeneity in banks’ resilience to climate shocks, with a focus on institutional characteristics. It examines whether attributes such as size, capitalization, loan portfolio composition, and geographic diversification enhance resilience or, conversely, increase vulnerability to physical risks induced by climate anomalies. By exploring these interactions, the analysis highlights the structural determinants of financial stability in the face of climate-related disruptions.

The structure of the U.S. banking sector—shaped by decades of consolidation and regulatory change—offers a compelling setting for examining resilience to climate-induced risks. In recent years, debates over bank mergers and market concentration have taken center stage (Oberfield et al., 2024). Some argue that larger institutions, with broader geographic footprints and more diversified loan portfolios, are better equipped to absorb localized shocks, including those triggered by climate disruptions. However, the unique nature of physical risks associated with climate shocks, such as those induced by ENSO, raises questions about the actual ability of large banks to effectively mitigate these impacts.

To formally assess these interactions, we employ the following dynamic panel specification:

$$ \Delta_h y_{i,t+h}^k = \lambda_h^k y_{i,t-1}^k + \kappa_h^k \Phi_{i,t} + \omega_h^k r_{i,t}^{niño} + \alpha_h^k r_{i,t}^{niña} + \zeta_h^k (r_{i,t}^{niño} \times \Phi_{i,t}) + \xi_h^k (r_{i,t}^{niña} \times \Phi_{i,t}) \\ + \Gamma_h^k X_{i,t} + \phi_{h,i}^k + q_{h,t}^k + u_{h,i,t}^k \quad (10) $$

Here, $y_{i,t}^k$ denotes various banking outcomes, such as financial stability, performance, or credit risk measures, while $\Phi_{i,t}$ captures bank-specific characteristics of interest. The lagged dependent variable, $\lambda_h^k y_{i,t-1}^k$, captures persistence in banking outcomes, reflecting the inertia inherent in financial conditions. ENSO-induced shocks $r_{i,t}^{niño}$ and $r_{i,t}^{niña}$ measure the magnitude of El Niño and La Niña events, respectively, while accounting for each bank’s specific absolute teleconnection exposure as in equation (7). The interaction terms, $\zeta_h^k$ and $\xi_h^k$, capture how bank characteristics modulate the effects of ENSO shocks across time horizons $h$. Bank-level fixed effects, $\phi_{h,i}^k$, control for time-invariant heterogeneity across institutions, while quarter fixed effects, $q_{h,t}^k$, account for seasonal effects.

The analysis considers several bank-specific characteristics—size, capitalization, loan portfolio composition, and geographic diversification—each examined independently to assess its role in shaping resilience to climate shocks. Size, measured by the log of total assets, captures both potential economies of scale and risks linked to market concentration. Loan portfolio composition focuses on the share of real estate loans, which are particularly sensitive to climate-related disruptions. Geographic diversification is proxied by the number of states or counties in which a bank operates branches, reflecting exposure to spatially heterogeneous
<table><caption>**Table 2:** Average temperature teleconnections by bank size</caption><thead><tr><th></th><th>Small banks<br>(&lt;1bn)</th><th>Large banks<br>(10-25bn)</th><th>Large banks<br>(≥ 25bn)</th><th>All banks</th></tr></thead><tbody><tr><th>τ<sup>niño</sup><sub>i,t</sub></th><td>0.026 (0.045)</td><td>0.019 (0.048)</td><td>0.018 (0.047)</td><td>0.022 (0.042)</td></tr><tr><th>τ<sup>niña</sup><sub>i,t</sub></th><td>0.088 (0.059)</td><td>0.071 (0.082)</td><td>0.060 (0.094)</td><td>0.075 (0.076)</td></tr></tbody></table>

Note: This table reports average temperature teleconnections estimates $\tau_{i,t}^g$ by bank size along with standard deviation in parenthesis, capturing the asymmetric impacts of *El Niño* and *La Niña*. Teleconnections reflect the varying climatic exposures faced by banks of different sizes. Estimates are based on the methodologies proposed by Callahan and Mankin (2023) and Liu et al. (2023). See Section 3 for more details.

teleconnection patterns. By incorporating these structural features, the analysis contributes to the broader understanding of how institutional characteristics mediate the financial effects of climate variability.

**David vs. Goliath: how size shapes resilience.** This first exercise assesses whether bank size influences the response to ENSO-induced climate shocks. Although larger institutions are often perceived as less vulnerable due to their diversification, the nature of physical climate shocks challenges this conventional wisdom. Climate events such as ENSO often affect broad geographic areas, and even large banks may find their operations concentrated in regions characterized by high teleconnection intensities. As shown in Table 2, large banks are, on average, no less exposed to ENSO-related local anomalies, suggesting that their geographic reach does not necessarily mitigate climate risk. Despite their scale, these banks frequently exhibit significant exposure to vulnerable sectors like real estate, which can exacerbate their susceptibility to systemic risks.

Based on average asset levels over the sample period, we classify banks into two groups: small banks (assets below $250 million), typically local institutions with limited branch networks, and large banks (assets above $1 billion), generally perceived as more resilient due to greater diversification and resource buffers. This classification enables us to examine whether larger institutions are better positioned to absorb climate-related financial disruptions. Figure 11 presents the results, highlighting distinct patterns in stability and risk exposure across the two groups. While small banks account for the majority of the sample, large banks comprise only 3.5% to 19.5% of institutions depending on the period, resulting in somewhat noisier estimates for the latter. Moreover, the large-bank group exhibits greater heterogeneity, encompassing institutions with varying degrees of geographic diversification and business models.

Focusing on *El Niño* shocks, Panel A of Figure 11 shows the trajectory of overall bank stability, as measured by the Z-score. The results suggest a visible destabilizing effect for small banks, while the impact on large banks appears more muted. However, decomposing the Z-score into its performance (return on assets) and solvency (equity-to-assets ratio) components does not reveal a strong divergence in dynamics between the two bank groups. This suggests that *El Niño* shocks do not generate significantly different effects across size categories, consistent with the finding that large banks, on average, face similar levels of local climate exposure (Table A3).

Figure 11 presents a nuanced picture. While small banks are visibly destabilized by *El Niño* shocks—as reflected in significant declines in Z-scores—large banks initially appear more insulated. However, this apparent resilience fades under *La Niña* conditions. In that case, large institutions experience a more rapid and pronounced deterioration in financial stability, primarily driven by a surge in non-performing loans (NPLs). Strikingly, their NPLs increase by nearly twice as much as those of small banks, suggesting heightened exposure to adverse credit dynamics.

Overall, these results reinforce the patterns observed in the baseline estimations (Section 5) and the local climate exposure analysis (Section 6). The findings confirm that *La Niña* shocks generate stronger and more persistent disruptions than *El Niño*, and they highlight that climate-related financial instability is not exclusively driven by small banks. Instead, larger institutions, despite their scale, remain highly exposed to ENSO-induced risks, particularly through their lending portfolios and their concentration in climate-
A

El Niño

B

La Niña

<figure><img src="image_16.png" /></figure>

<figure><img src="image_17.png" /><figcaption>Figure 11: ENSO shocks by bank size</figcaption></figure>

Note: Impulse Response Functions (IRFs) based on equation (10) estimated using the Local Projections (LP) methodology (Jordà, 2005, 2023; Jordà and Taylor, 2025). Shaded areas denote 90% confidence intervals, with robust standard errors clustered at the bank level to account for intra-group dependence and ensure valid inference. IRFs report cumulative changes expressed in percentage points (pp), except for the Z-score, which is presented as cumulative percentage changes (%).

A

El Niño

B

La Niña

<figure><img src="image_18.png" /></figure>

<figure><img src="image_19.png" /><figcaption>Figure 12: ENSO shocks by capitalization</figcaption></figure>

Note: Impulse Response Functions (IRFs) based on equation (10) estimated using the Local Projections (LP) methodology (Jordà, 2005, 2023; Jordà and Taylor, 2025). Shaded areas denote 90% confidence intervals, with robust standard errors clustered at the bank level to account for intra-group dependence and ensure valid inference. IRFs report cumulative changes expressed in percentage points (pp), except for the Z-score, which is presented as cumulative percentage changes (%).
sensitive regions.

These findings challenge the conventional view that larger financial institutions are inherently more resilient due to scale or diversification. As Acemoglu et al. (2015) demonstrate, in highly interconnected systems, diversification can fail to contain systemic risk when exposures are correlated across institutions. ENSO-related shocks, by synchronizing borrower distress and asset devaluation across regions, undermine the effectiveness of scale and geographic spread as protective mechanisms. In this context, size may amplify rather than buffer climate-induced financial fragility.

This evidence also points to a trade-off between efficiency and resilience. Large banks may benefit from economies of scale, broader market reach, and streamlined operations—features typically associated with financial efficiency. However, the very features that confer competitive advantages under normal conditions may become liabilities during systemic climate events, as geographic diversification and portfolio breadth fail to offset correlated risks. This trade-off underscores the importance of re-evaluating assumptions about optimal bank structure in light of growing climate-related vulnerabilities.

**Increased capitalization, increased resilience?** We now investigate whether higher capitalization improves banks’ resilience to ENSO-induced climate shocks. Capitalization is a cornerstone of financial stability, as a stronger equity base acts as a buffer against unexpected losses, reducing the likelihood of financial distress. A priori, one might expect well-capitalized banks to demonstrate greater stability in response to climate shocks. A higher equity buffer acts as a cushion against unexpected losses and reduces the likelihood of financial distress, particularly during periods of credit deterioration. This expectation is grounded in prudent regulation principles, such as Basel III, which imposes minimum capital requirements precisely to enhance resilience under adverse conditions (Berger, 1995; Laeven and Levine, 2009).

To test this channel, we separate banks into low-capital (bottom quartile, EQ < 8%) and high-capital (top quartile, EQ > 12%) groups. This classification allows us to examine whether capital buffers translate into different stability outcomes when banks are exposed to ENSO-induced climate shocks. The results, displayed in Figure 12 provide insights into the extent to which capitalization moderates climate-related financial stress.

The empirical results, reported in Figure 12, challenge the conventional assumption that stronger capitalization systematically enhances resilience. While the overall trajectories of key stability indicators—such as the Z-score, ROA, and the equity ratio—are consistent with the patterns observed in the baseline estimations, there are no statistically significant differences between the two capitalization groups. Both high- and low-capitalized banks exhibit similar declines in performance and solvency following La Niña shocks, with overlapping confidence intervals across the projection horizon.

This absence of heterogeneity suggests that the protective role of capital buffers may be limited when facing climate-induced financial stress. One possible interpretation is that climate shocks, unlike idiosyncratic or purely macroeconomic downturns, operate through transmission channels that capital ratios alone cannot insulate against. These include abrupt deteriorations in credit quality, losses on climate-sensitive asset classes (e.g., real estate), and operational disruptions that undermine revenue generation. Consequently, while capitalization remains a fundamental pillar of bank resilience, its mitigating power appears to be insufficient when facing geographically widespread, systemically synchronized shocks such as ENSO events.

**Loan exposure shapes sensitivity to climate shocks.** We now turn to the composition of banks’ loan portfolios as a potential determinant of their resilience to ENSO-induced climate shocks. Lending is the primary asset-side activity through which financial institutions are exposed to macroeconomic volatility and environmental disruption. Accordingly, this section investigates whether the structure of a bank’s credit exposure amplifies its vulnerability to climate-related financial instability.

We focus on two key dimensions of loan exposure. First, we examine the overall share of gross loans in total assets—a commonly used proxy for balance sheet credit intensity (Figure 13). Second, we isolate real estate lending by computing the share of real estate loans in the bank’s loan book (Figure 14). These two indicators capture, respectively, the general reliance on lending as a business model and the exposure to a
climate-sensitive asset class. For each variable, we split the sample by quartile, comparing the top 25% of banks (high exposure) with the bottom 25% (low exposure).

The first experiment focuses on the overall loan-to-asset ratio, as illustrated in Figure 13. This specification directly tests whether banks more reliant on lending exhibit heightened sensitivity to climate disruptions. The results reveal a clear asymmetry between the two ENSO phases. Banks with low loan-to-asset ratios show relatively modest and symmetric responses to both *El Niño* and *La Niña* shocks, suggesting that other transmission mechanisms are at play. In contrast, banks with high credit intensity experience pronounced destabilization under *La Niña*, with impacts nearly twice as large as those observed for low-exposure banks. These effects are primarily driven by a substantial increase in non-performing loans and foreclosure rates, underscoring the role of lending as a key conduit of climate-related financial stress.

Finally, banks with high loan-to-asset ratios effectively serve as amplifiers of ENSO disruptions, as climate-induced borrower distress and collateral degradation interact to deepen balance sheet deterioration. These findings resonate with systemic fragility models in which leveraged institutions, despite appearing solvent under normal conditions, become highly vulnerable when asset values and repayment capacities co-move negatively under stress.

We refine this analysis by isolating the impact of real estate lending, as shown in Figure 14. Real estate loans represent a particularly salient risk channel, as they are typically backed by physical collateral that can be directly impaired by climate anomalies. Prior episodes of extreme weather have shown that disruptions to agricultural, residential, or commercial land use can significantly depress property values and impair repayment capacity. Moreover, real estate portfolios often include assets such as farmland, infrastructure, and commercial developments—sectors that are acutely sensitive to weather variability.

The results confirm that real estate loan exposure is a key determinant of financial fragility in response to ENSO-induced climate shocks—particularly during *La Niña* phases. Banks with high concentrations of real estate lending experience the sharpest declines in stability metrics and the most pronounced increases in credit risk. These effects stem from the dual vulnerability of both borrowers and collateral: climate anomalies impair repayment capacity while simultaneously reducing the value of underlying assets. In contrast, banks with limited exposure to real estate lending exhibit considerably more muted responses, underscoring the critical role of loan portfolio composition in shaping resilience to climate-related disruptions.

Taken together, these findings highlight that loan portfolio structure—notably the intensity of lending and sectoral allocation—is a decisive factor in conditioning banks’ exposure to climate risk. Unlike capital buffers or banks’ size, which show limited predictive power in this context, lending patterns—especially toward real estate—emerge as robust amplifiers of ENSO-induced financial stress. These results underscore the need for climate-adjusted risk models and stress testing tools that incorporate granular, sector-specific credit exposures.

**A false sense of geographical diversification.** We now assess whether geographical diversification provides effective protection against ENSO-induced climate shocks. To this end, we compare banks with a single branch to those in the top quartile of branch network size—institutions operating at least six branches. The underlying hypothesis is that a broader geographic footprint should, in theory, mitigate localized climate risks through diversification and risk pooling. However, as suggested in our earlier analysis of bank size, institutions with larger networks do not necessarily exhibit greater resilience.

Figure 15 presents the results of this heterogeneity analysis. Contrary to conventional expectations, banks with more branches appear more vulnerable to *La Niña* shocks than their single-branch counterparts. Specifically, multi-branch banks experience sharper and more persistent declines in stability metrics, suggesting that geographic expansion does not effectively hedge climate-related financial disruptions.

Two key factors likely account for this pattern. First, as shown in Figure 6, ENSO events affect large, contiguous regions, limiting the benefits of spatial diversification. Moreover, ENSO anomalies typically unfold over several months, increasing the likelihood that seemingly diversified regions will be affected simultaneously or in overlapping waves. For geographic dispersion to provide a meaningful buffer, banks would
<figure><img src="image_20.png" /><figcaption>Figure 13: ENSO shocks by gross loan ratio</figcaption></figure>

Note: Impulse Response Functions (IRFs) based on equation (10) estimated using the Local Projections (LP) methodology (Jordà, 2005, 2023; Jordà and Taylor, 2025). Shaded areas denote 90% confidence intervals, with robust standard errors clustered at the bank level to account for intra-group dependence and ensure valid inference. IRFs report cumulative changes expressed in percentage points (pp), except for the Z-score, which is presented as cumulative percentage changes (%).

<figure><img src="image_21.png" /><figcaption>Figure 14: ENSO shocks by real estate loan ratio</figcaption></figure>

Note: Impulse Response Functions (IRFs) based on equation (10) estimated using the Local Projections (LP) methodology (Jordà, 2005, 2023; Jordà and Taylor, 2025). Shaded areas denote 90% confidence intervals, with robust standard errors clustered at the bank level to account for intra-group dependence and ensure valid inference. IRFs report cumulative changes expressed in percentage points (pp), except for the Z-score, which is presented as cumulative percentage changes (%).
<figure><img src="image_22.png" /><figcaption>Figure 15: ENSO shocks by branch number</figcaption></figure>

Note: Impulse Response Functions (IRFs) based on equation (10) estimated using the Local Projections (LP) methodology (Jordà, 2005, 2023; Jordà and Taylor, 2025). Shaded areas denote 90% confidence intervals, with robust standard errors clustered at the bank level to account for intra-group dependence and ensure valid inference. IRFs report cumulative changes expressed in percentage points (pp), except for the Z-score, which is presented as cumulative percentage changes (%).

need to operate across states with uncorrelated climate exposure—an assumption that does not hold in the U.S. context. These dynamics reflect a broader insight from systemic risk literature: spatially correlated shocks—such as those triggered by large-scale climate anomalies—undermine the very logic of diversification, turning presumed buffers into amplifiers under stress. In practice, even among large institutions, geographic reach remains limited: banks with over $25 billion in assets operate, on average, in just nine states (Table 1).

Second, single-branch banks may benefit from superior local information and stronger borrower relationships. These institutions often rely on in-depth knowledge of their communities, enabling them to assess risk more accurately and respond more effectively to environmental and economic stressors. This is consistent with the literature on relationship banking, which emphasizes the role of proximity and trust in promoting prudent lending practices and mitigating default risk (Petersen and Rajan, 1994). In contrast, larger banks with standardized credit models may fail to detect localized vulnerabilities, increasing their exposure to climate-driven losses.

Overall, these results challenge the notion that geographical diversification inherently enhances resilience to physical climate risk. While spatial dispersion may reduce exposure to idiosyncratic shocks, its effectiveness is limited in the face of large-scale, synchronized climate anomalies such as ENSO. Instead, our findings underscore the importance of localized risk management strategies—anchored in community knowledge and adaptive lending practices—as a more reliable foundation for climate resilience. These insights call for a reevaluation of how climate correlation structures are accounted for in internal risk models and supervisory stress testing frameworks. In particular, assumptions about diversification benefits should be reexamined when dealing with climate shocks of systemic and regional scale.

While recent evidence suggests that spatial diversification can attenuate climate-related risks in mortgage-backed securities (Kahn et al., 2024), our analysis indicates that such protection may not uniformly extend to broader banking operations. Unlike structured portfolios specifically engineered to diversify geographically,
banks' lending exposures—particularly to real estate and agriculture—may remain highly correlated across regions under ENSO conditions. This observation highlights potential limitations of diversification as a shield against climate-induced systemic shocks, especially when risk materializes across interdependent markets simultaneously.

# 8 Ruling-Out Alternative Explanations

We conduct a comprehensive set of robustness checks to confirm the stability of our baseline estimates and rule out alternative explanations. These exercises test whether the observed effects are driven by methodological choices, regulatory or institutional heterogeneity, financial health differences across banks, or broader macroeconomic conditions. Specifically, we assess the sensitivity of our results to different shock definitions, additional fixed effects, sample restrictions, and expanded control sets. Across all specifications, the results remain qualitatively consistent, reinforcing the conclusion that ENSO-induced banking instability is not an artifact of model specification or broader economic confounders. The persistence of these effects across multiple robustness exercises highlights the distinct role of climate shocks in shaping financial stability, further validating the transmission channels identified in our main analysis. All supporting results are presented in Section A4 of the Appendix.

**Baseline binary shocks.** To test whether our results are driven by the choice of shock measurement, we replace the continuous ENSO shock variable with a binary specification. Instead of relying on the UECI-based magnitude measure, we define ENSO events using a threshold of $r \ge 2$, distinguishing between *El Niño* and *La Niña* based on their phase angles:

$$1_t^{\text{niño}} = \begin{cases} 1 & \text{if } r_t \ge 2 \quad \text{and } \theta \in [180^{\circ}; 360^{\circ}] \\ 0 & \text{otherwise} \end{cases}$$

$$1_t^{\text{niña}} = \begin{cases} 1 & \text{if } r_t \ge 2 \quad \text{and } \theta \in [0^{\circ}; 180^{\circ}] \\ 0 & \text{otherwise} \end{cases}$$

$$\Delta_h Y_{i,t+h}^k = \lambda_h^k Y_{i,t-1}^k + \omega_h^k 1_t^{\text{niño}} + \alpha_h^k 1_t^{\text{niña}} + \Gamma_h^k X_{i,t} + \phi_{h,i}^k + q_{h,t}^k + u_{h,i,t}^k$$

This alternative approach captures the occurrence of strong climate shocks but does not account for variations in their intensity. The results remain qualitatively similar, confirming the robustness of our findings. However, estimates are slightly noisier, reflecting the loss of information from the shock magnitude dimension.

**Federal districts.** To account for regulatory and supervisory differences that may influence banking outcomes, we introduce fixed effects at the Federal Reserve District level. The U.S. banking system is segmented into twelve Federal Reserve Districts, each responsible for overseeing banks in its jurisdiction. These districts play a significant role in regulatory enforcement and monetary policy implementation, potentially shaping how banks respond to climate shocks. Including district-level controls does not materially alter our results, indicating that regional regulatory differences do not drive the observed ENSO-induced financial instability.

**States Fixed Effects.** Since U.S. banking regulation is partly determined at the state level—particularly for state-chartered banks—we further introduce state fixed effects to control for local regulatory variations. States impose distinct capital requirements, lending restrictions, and supervision practices that could influence bank resilience to climate shocks. By accounting for these institutional differences, we ensure that our findings are not confounded by state-specific banking policies. The results remain robust, confirming that the observed banking instability is primarily driven by climate-induced disruptions rather than state-level regulatory heterogeneity.

**Survival banks.** In the baseline estimates, we include all commercial banks in our analysis, even if they fail during the sample period. Excluding failed banks could introduce survival bias, leading to an overestimation of resilience and an underestimation of climate shock effects. To assess the potential impact of
this bias, we conduct two robustness checks. First, we re-estimate the models using a subsample of banks that survived throughout the period. Second, based on the overall sample, we introduce a binary indicator distinguishing between continuously operating banks and those that failed. In both cases, the results remain qualitatively unchanged, with slightly lower magnitude, highlighting that bank failure should be integrated when considering bank stability and suggesting that survival bias does not distort our main conclusions.

**Banking controls (CAMELS).** Our specification prioritizes parsimony, relying on the exogenous nature of ENSO shocks to ensure clean identification by avoiding the inclusion of “bad regressors” that may introduce noise and undermine causal inference (Angrist and Pischke, 2009; Brei et al., 2024). However, to avoid endogeneity concerns related to the omission of important banks’ characteristics variables, we conduct a robustness check by incorporating CAMELS indicators to verify that our results are not driven by broader differences in financial health across banks. This way, we ensure that the estimated effects of climate shocks are not confounded by contemporaneous changes in bank fundamentals.

The CAMELS framework is a widely used regulatory tool for assessing bank soundness, covering key dimensions of financial stability. In this robustness exercise, we include equity-to-assets for capitalization, return on assets for profitability, cost-to-income ratio for operational efficiency, net interest margins for lending profitability, deposits-to-assets for liquidity, and non-interest income relative to total assets to gauge exposure to market fluctuations. These additional controls do not alter our main findings, confirming that ENSO shocks have a distinct impact on banking stability, independent of broader financial health factors.

**Subprime confounding factor.** Given that our sample spans the 2008 financial crisis, we ensure that our results are not driven by the subprime mortgage crisis. All baseline estimates already include a control for the NBER-defined subprime crisis period. As an additional robustness check, we conduct two exercises. First, we estimate the models without the subprime control to verify whether the financial crisis period disproportionately influences the results. Second, we restrict the sample to the pre-crisis period and re-estimate the models with a conservative definition of the crisis window, with estimates covering the 1994Q1-2006Q4 period. In both cases, results remain qualitatively unchanged, confirming that the observed effects are distinct from those associated with the subprime crisis.

**Seasonality.** Banking data is subject to seasonal fluctuations, particularly in loan origination, deposit growth, and income reporting. To address seasonality, our baseline estimates incorporate quarter fixed effects and transform dependent variables using a four-quarter uncentered moving average, a standard approach in time-series econometrics. To assess the sensitivity of our results to this specification choice, we re-estimate the models without the moving average transformation. While the results appear slightly more erratic due to short-term fluctuations, the overall shock dynamics remain intact, confirming that our main conclusions are not driven by seasonal effects.

**Macroeconomic business cycles.** To ensure that the estimated effects are not driven by broader macroeconomic conditions, we introduce additional controls for key macroeconomic indicators, including real GDP growth, inflation, and unemployment rates, all evaluated at the county level. These variables capture the overall business cycle, helping to distinguish climate shock effects from broader economic fluctuations. The results remain robust, indicating that the financial instability induced by ENSO shocks is not merely a confounding reflection of cyclical economic downturns.

**Precipitation placebo test.** As a complementary placebo test, we extend our local exposure estimation to precipitation-based teleconnections to examine whether ENSO-induced banking instability could arise through channels other than temperature anomalies. Results are presented in Appendix A5 and show that the estimated precipitation teleconnection coefficients are markedly noisier and more spatially fragmented, consistent with the volatile and localized nature of precipitation patterns. The corresponding regression results reveal no systematic or economically significant effects on bank stability, reinforcing the conclusion that temperature anomalies—rather than precipitation shocks—constitute the primary transmission channel through which ENSO affects financial outcomes.
**Liquidity channel.** While liquidity risk is often highlighted as a key vulnerability in developing economies, where less mature financial markets and limited deposit insurance can lead to substantial withdrawals in response to climate shocks (Brei et al., 2019; Chabot and Bertrand, 2023), it is generally considered a secondary concern in advanced banking systems. In the U.S. context, robust regulatory safeguards and deposit insurance mechanisms effectively mitigate the risk of liquidity-driven destabilization. Accordingly, our analysis centers on credit risk and profitability as the primary transmission channels. To assess liquidity dynamics, we examine the ratio of deposits to total assets, a key indicator of short-term funding structure. Results are presented in Appendix A6 and show a modest and short-lived increase in deposit ratios following *El Niño* episodes, which is economically insignificant. For *La Niña*, where broader financial stress is observed, we detect a larger yet still moderate 0.6pp rise—equivalent to a 0.7% increase in deposits—likely reflecting insurance payouts or potential government assistance. Overall, these findings suggest that liquidity risk is not a driver of banking fragility in response to ENSO-induced climate shocks.

# 9 Conclusion

This study provides compelling evidence that ENSO-induced climatic shocks significantly impact U.S. banking stability, with effects that—while not as extreme as those experienced during the global financial crisis—are nonetheless comparable in direction and persistence to those observed during the early quarters of the COVID-19 pandemic. In particular, the decline in profitability and the rise in foreclosure and non-performing loan ratios following strong *La Niña* episodes point to a non-negligible source of medium-term systemic risk. Previous climate research has shown that treating *El Niño* as merely the inverse of *La Niña* oversimplifies their distinct characteristics (Hoerling et al., 1997). By employing a nonlinear approach, we offer a more flexible specification than traditional linear models, which typically constrain the effects of *El Niño* and *La Niña* to be symmetric. Relaxing this constraint allows for a more accurate estimation of their asymmetric impacts, revealing that *La Niña*, characterized by stronger teleconnections, exerts notably greater adverse effects on bank stability than *El Niño*.

ENSO-induced disruptions primarily threaten financial stability through heightened credit risks, including increases in non-performing loans, reduced profitability, and elevated solvency concerns. These outcomes highlight loan portfolios as a critical transmission channel for climate-induced financial shocks. Moreover, ongoing global warming is likely to alter ENSO dynamics, potentially increasing the frequency and intensity of these climatic events, thereby intensifying the systemic risks faced by the financial sector (Cai et al., 2018, 2021; Lopez et al., 2022).

The analysis shows that certain features of U.S. banks’ business models systematically increase their vulnerability to ENSO-induced climate shocks—particularly during *La Niña* episodes. Large banks with concentrated geographic footprints, and those with a high share of real estate lending, tend to experience greater financial stress. This finding challenges the conventional view that size necessarily confer resilience, and instead suggests that these traits may amplify exposure when operations are concentrated in climate-sensitive regions. These results underscore the need for climate risk management strategies that explicitly account for localized exposure—especially to persistent temperature anomalies—and support the integration of climate considerations into prudential supervision. In doing so, the study contributes to the growing literature at the intersection of climate change and financial stability, offering both policy-relevant insights and directions for further research.

From a policy perspective, our results suggest that integrating climate risks into prudential regulation and stress-testing frameworks requires more than broad-based tools. Regulatory approaches must explicitly account for heterogeneity in exposure and institutional capacity. In particular, banks with concentrated geographic footprints or high exposure to climate-sensitive sectors—such as agriculture and real estate—may require tailored supervisory mechanisms to prevent the amplification of localized shocks into broader financial instability. As climate science and computational capacity continue to advance, more accurate and timely forecasts of ENSO variability are likely to become available. These improvements could significantly
strengthen the ability of financial supervisors to anticipate region-specific vulnerabilities and implement
forward-looking, countercyclical prudential tools—such as dynamic capital buffers—before systemic risks
materialize.

Moreover, the prominence of real estate lending as a conduit for systemic risk warrants careful attention. Traditional securitization practices, along with instruments such as Credit Risk Transfers (CRTs), can amplify financial vulnerabilities by shifting risk to non-bank entities or by increasing leverage within originating banks. These dynamics evoke historical concerns from the 2008 financial crisis and highlight the persistent challenges of managing climate-related risks within highly interconnected financial systems

Future research should further explore long-term projections under multiple global warming scenarios, providing valuable insights into evolving ENSO patterns and their potential implications for financial stability. Additionally, investigating how regulatory constraints on banking structures influence resilience to climate shocks, particularly in vulnerable regions, would offer deeper insights.

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# Appendices

## A1 Climate Data

### A1.1 ENSO index

The *El Niño*-Southern Oscillation (ENSO) is a naturally occurring climate pattern resulting from interactions between the ocean and atmosphere in the tropical Pacific. This phenomenon manifests as periodic variations in sea surface temperatures (SST), wind patterns, and pressure systems. ENSO has two primary phases: *El Niño*, associated with unusually warm SST in the central and eastern Pacific, and *La Niña*, which corresponds to cooler-than-average SST in the same regions. These phases influence global weather patterns through a process known as teleconnection, where climate anomalies in one part of the world induce changes in the climate of distant regions. Teleconnections can lead to significant changes in precipitation, temperature, and storm activities globally, reflecting the interconnected nature of Earth's climate systems. Consequently, ENSO events can have profound effects on ecosystems, human health, and economic activities, particularly in sectors such as agriculture, fisheries, and water management (Generoso et al., 2020; Smith and Ubilava, 2017).

Economic studies analyzing the impact of ENSO often rely on indices like the NINO3.4 or the Oceanic Niño Index (ONI) to capture SST anomalies in the NINO3.4 region, which spans from the Central Pacific (CP) to the Eastern Pacific (EP). However, these standard indices have limitations in distinguishing between two distinct types of ENSO events—Central Pacific (CP) and Eastern Pacific (EP)—because they aggregate data over broad regions, often obscuring critical differences between these ENSO variants. This challenge arises because anomalies detected in these regions frequently extend into neighboring areas, creating spatial overlap and diagnostic interference. This limitation has become more pronounced as recent climatology research suggests that ENSO variability is becoming increasingly diverse due to climate change (Kim and Yu, 2012). This increased diversity includes variations in both the intensity and the location of the anomalies associated with ENSO.

Recent studies tend to differentiate between CP and EP types of ENSO events because they produce markedly different climate patterns and, consequently, different economic impacts (Geng et al., 2022). Central Pacific (CP) *El Niño* events, also referred to as “Modoki *El Niño*,” are centered around the NINO4 region (160°E-150°W), where warming occurs primarily in the central part of the Pacific Ocean (see Figure A1 for the spatial definitions of ENSO regions). These events tend to have weaker impacts on coastal regions but can significantly influence weather patterns in places like North America and Southeast Asia. In contrast, Eastern Pacific (EP) *El Niño* events, which are more typical, cause intense warming in the NINO1+2 and NINO3 regions (80°W-90°W and 90°W-150°W respectively), affecting the eastern tropical Pacific and leading to more pronounced climate anomalies, such as extreme rainfall or droughts, in South America and across the tropical belt.

To better account for the evolving complexity of ENSO and its broader economic impacts, new indices have been developed. For instance, the Trans-Niño Index (TNI), introduced by Trenberth and Stepaniak (2001), measures the temperature gradient between the central and eastern Pacific. It offers a clearer distinction between EP and CP ENSO events by subtracting NINO4 values from those of NINO1+2. Additionally, the *El Niño* Modoki Index (EMI), proposed by Ashok et al. (2007), highlights central Pacific anomalies while considering variations in the eastern and western Pacific. This index is further refined in the Improved Modoki Index (IEMI) by Li et al. (2010) to more effectively monitor weaker CP ENSO events.

In this article, we use the Unified ENSO Complex Index (UECI), a state-of-the-art indicator, to detect ENSO signals across multiple regions. Proposed by Zhang et al. (2019), the UECI combines two metrics that assess the strength and type of ENSO events. It effectively captures the complex dynamics of *El Niño* and *La Niña* by incorporating SST anomalies from the NINO3 and NINO4 regions of the Pacific Ocean.
<figure><img src="image_23.png" /><figcaption>Figure A1: El Niño Southern Oscillation regions</figcaption></figure>

Note: This map shows the key regions used to monitor and predict El Niño and La Niña events. These regions are defined by specific sea surface temperature anomalies (SSTa) in the tropical Pacific Ocean. The NINO3.4 region (marked as NINO3+4 in this plot) is the most commonly used indicator for classifying El Niño / La Niña events, while NINO1+2 provides early signals of changes near South America. SST anomalies in the NINO3 and NINO4 regions are mainly used to monitor different types of ENSO events, such as Eastern Pacific (EP) El Niño, Central Pacific (CP) El Niño, and La Niña.

SST data are retrieved from the ERA5 reanalysis dataset on a monthly basis from 1950 to 2021. We compute climatological anomalies by determining the deviation of each month's SST from its average during a reference period for the NINO3 and NINO4 regions.

The UCEI is designed to capture both the intensity and spatial distribution of sea surface temperature (SST) anomalies associated with El Niño-Southern Oscillation (ENSO) events. To achieve a comprehensive representation, the UCEI is defined in a two-dimensional plane analogous to the complex plane used in mathematics. In this framework, the horizontal axis represents the “real” part, and the vertical axis represents the “imaginary” part of the index. These components are formulated using the SST anomalies from the NINO3 and NINO4 regions, denoted as N3 and N4, respectively. The UCEI components are defined as:

$$ \text{Real part} = N3 + N4, \qquad (11) $$

$$ \text{Imaginary part} = N3 - N4, \qquad (12) $$

Where N3 and N4 denotes the SST anomalies over the NINO3 and NINO4 regions respectively.

The real part (sum of anomalies) captures the overall strength or magnitude of the ENSO-related temperature anomalies when considering both regions together. It effectively quantifies the combined warming or cooling across the central and eastern equatorial Pacific Ocean. By summing N3 and N4, we obtain a scalar quantity that reflects the cumulative impact of SST anomalies, providing a single metric for the overall ENSO intensity. The imaginary part (difference in anomalies) provides insights into the spatial distribution and disparity of ENSO effects between the eastern and central Pacific. A positive value indicates stronger anomalies in the NINO3 region, while a negative value suggests stronger anomalies in the NINO4 region. This difference highlights the zonal (east-west) gradient of SST anomalies, which is crucial for distinguishing between different types of ENSO events. The determination of the ENSO type according the real and imaginary parts can be summarized as follows:

$$ N3 + N4 > 0 \ \& \ N3 - N4 > 0 \ \text{EP El Niño} \qquad (13) $$

$$ N3 + N4 > 0 \ \& \ N3 - N4 < 0 \ \text{CP El Niño} \qquad (14) $$

$$ N3 + N4 > 0 \ \& \ N3 - N4 \approx 0 \ \text{Mixed El Niño} \qquad (15) $$

$$ N3 + N4 < 0 \ \& \ N3 - N4 < 0 \ \text{EP La Niña} \qquad (16) $$

$$ N3 + N4 < 0 \ \& \ N3 - N4 > 0 \ \text{CP La Niña} \qquad (17) $$

$$ N3 + N4 < 0 \ \& \ N3 - N4 \approx 0 \ \text{Mixed La Niña} \qquad (18) $$

Figure A2 illustrates the effectiveness of the UCEI in capturing the different ENSO types by depicting the composite SST anomalies averaged over months with typical ENSO events. The figure clearly demonstrates
<figure><img src="image_24.png" /><figcaption>Figure A2: Spatial patterns of Sea Surface Temperature anomalies by type and ENSO phases</figcaption></figure>

Note: Composites of SST anomalies during months characterized by strong (r > 2) El Niño (A, B) and La Niña (C, D) events. These composites were calculated by averaging the Sea Surface Temperature anomalies (SSTa) at each grid point for the selected months. The anomalies are defined relative to a climatological baseline, and represent deviations in SST from the normal conditions over the tropical Pacific Ocean.

that the “center of action” for La Niña events—the regions where SST anomalies are most pronounced— varies significantly along the east-west gradient depending on the ENSO type. This observation aligns with previous findings by Zhang et al. (2015), which noted that in EP La Niña events, the maximum cooling occurs in the eastern Pacific, whereas in CP La Niña events, the cooling is centered more towards the central Pacific region.

To facilitate practical applications, the UECI extends the use of the complex plane by applying it in polar form. Here, the magnitude (r) represents the overall strength of the ENSO event, while the phase angle (θ) provides a continuous measure of the ENSO type. This approach allows a succinct representation of ENSO events in a two-dimensional space, capturing both the intensity and spatial distribution of SST anomalies. The two parameters are defined as follows:

$$r = \sqrt{(\text{Real part})^2 + (\text{Imaginary part})^2} \qquad (19)$$

$$\theta = \begin{cases} \arctan\left(\frac{\text{Imaginary part}}{\text{Real part}} \cdot \frac{180}{\pi} + 270\right) & \text{with Real part} > 0 \\ \arctan\left(\frac{\text{Imaginary part}}{\text{Real part}} \cdot \frac{180}{\pi} + 90\right) & \text{with Real part} < 0 \end{cases} \qquad (20)$$

The ENSO phases and types of events categorized following Zhang et al. (2019). In the complex plane, El Niño EP events have angles between 285° and 360°, as the real component is positive and larger than the imaginary component. Conversely, EP La Niña events are typically the reverse of El Niño phases, with angles ranging between 0 and 75° in the complex plane. Mixed El Niño / La Niña events have phase angles that do not fit neatly into the EP or CP categories, as their SST anomalies are distributed more evenly between the NINO3 and NINO4 regions. The phase angles for mixed events fall between the values typically associated with EP and CP events, with 75° < θ < 105° for mixed La Niña and 255° < θ < 285° for mixed El Niño phases (Figure A3).
<figure><img src="image_25.png" /><figcaption>**Figure A3:** Magnitude (r) and phase angle (θ) components of the unified complex ENSO index</figcaption></figure>

Note: Time series of the magnitude component, *r* (A), of the phase component, *θ* (B) and scatter plots of the unified complex ENSO index (C). *La Niña* events are characterized by phase angles between 0° and 180°, while *El Niño* events occur for angles between 180° and 360°.

## A1.2 Asymmetric Effects and Teleconnections

Most studies on the economic impact of ENSO focus on the global effects of the phenomenon by simply distinguishing between the *El Niño* and *La Niña* phases, often treating these phases as having uniform impacts worldwide (Damette et al., 2024; Cashin et al., 2017; Smith and Ubilava, 2017; Berry and Okulicz-Kozaryn, 2008). However, it is well documented that ENSO variations produce heterogeneous effects on the global climate, leading to contrasting impacts within large areas like the United States (Cook et al., 2007; Rippey, 2015). ENSO causes significant regional differences in temperature anomalies. During *El Niño* events, southern states often experience cooler temperatures due to increased cloud cover and precipitation, whereas northern regions may have milder winters with above-average temperatures. Conversely, *La Niña* phases can bring warmer and drier conditions to the south and colder, wetter winters to the north. Ignoring this spatial heterogeneity and treating ENSO phases as having uniform effects can lead to biased estimates of ENSO's true impact because it overlooks critical regional variations that affect local economies differently.

Few economic studies investigating the economic consequences of ENSO have accounted for the phenomenon's heterogeneous effects across different regions. Generoso et al. (2020) address this gap by interacting ENSO phases with country-specific measures of dry and wet spells, providing a more nuanced understanding of its economic impacts. Callahan and Mankin (2023) and Liu et al. (2023) further capture ENSO's indirect effects on global climate patterns through teleconnection estimates. Teleconnections refer to large-scale climate anomalies that are statistically related over vast distances, often spanning thousands of kilometers. ENSO is a primary driver of such teleconnections, influencing atmospheric circulation patterns that affect weather and climate far from its origin in the tropical Pacific Ocean. These influences manifest as coherent patterns across different regions over the globe. To analyze these teleconnection patterns, climate researchers rely on high-resolution spatial and temporal datasets that combine historical weather observations with numerical weather prediction models. This approach produces consistent and spatially complete records of atmospheric variables, allowing for detailed mapping of ENSO-related climate anomalies.
In this article, we adopt the methodologies proposed by Callahan and Mankin (2023) and Liu et al. (2023) to estimate the local meteorological effects of ENSO across the contiguous United States. We calculate teleconnection indices focusing on the boreal winter season (December-January-February, DJF), when ENSO typically reaches its peak intensity, significantly influencing global temperature, and precipitation anomalies (Trenberth and Stepaniak, 2001). Following standard practice, we use the averaged magnitude of the UECI (r) over the DJF months, denoted $\bar{r}$, to capture these effects.

We use monthly data on surface air temperature and precipitation from the ERA5 land database over the period of 1950-2021 with a 0.1° spatial resolution. Anomalies are calculated by subtracting the long-term monthly averages (1950–2021) from the actual monthly values for each variable. The time series are detrended using a quadratic fit to remove long-term trends associated with climate change, ensuring that the anomalies reflect interannual variability rather than secular trends.

We account for the nonlinear effects of ENSO on local weather by including an interaction term between our DJF index, $\bar{r}$, and a dummy variable d indicating ENSO phases according to the mean phase angle of the UECI over DJF, denoted $\bar{\theta}$:

$$d = \begin{cases} 0 & \text{if } \bar{\theta} > 180^{\circ} \text{ (El Niño),} \\ 1 & \text{if } \bar{\theta} < 180^{\circ} \text{ (La Niña).} \end{cases} \qquad (21)$$

This allows us to capture differences in baseline levels and to let the effect of ENSO vary during these episodes. Climate researchers have shown that treating *El Niño* as the mirror image of *La Niña* is an oversimplification (Hoerling et al., 1997). Our nonlinear model, therefore, offers a more flexible specification than linear models, which constrain the effects of *El Niño* and *La Niña* to be equal in magnitude but opposite in sign. By relaxing this constraint, we can estimate the asymmetric impacts of *El Niño* and *La Niña* events on meteorological variables more accurately.

Given that ENSO events typically develop in year $t-1$, peak in winter, and decrease by summer of the following year $t$, we allow $\bar{r}$ to influence data from May of year $t-1$ through December of year $t$. We regress normalized monthly grid-point surface air temperature and precipitation onto $\bar{r}$, yielding fields of regression coefficients for each ENSO phase at each longitude-latitude grid point $(x, y)$ and calendar month $(m)$. Following Liu et al. (2023), we include a vector of weather variables $c'$ to control for the covariance between temperature and precipitation when analyzing each variable. Teleconnections are estimated using Generalized Least Squares (GLS) with an autoregressive order 1 (AR(1)) process to account for serial correlation in the residuals, which may arise from persistent meteorological patterns not fully explained by ENSO. The estimated models can be written as follows:

$$T_{x,y,m,t} = \alpha_{x,y,m}^{T} + \eta_{x,y,m}^{T} \bar{r} t + \gamma_{x,y,m}^{T} d_{t} + \delta_{x,y,m}^{T} (\bar{r} t \cdot d_{t}) + \phi_{x,y,m}^{T} c'_{x,y,m,t} + v_{x,y,m,t}^{T} \qquad (22)$$

$$P_{x,y,m,t} = \alpha_{x,y,m}^{P} + \eta_{x,y,m}^{P} \bar{r} t + \gamma_{x,y,m}^{P} d_{t} + \delta_{x,y,m}^{P} (\bar{r} t \cdot d_{t}) + \phi_{x,y,m}^{P} c'_{x,y,m,t} + v_{x,y,m,t}^{P} \qquad (23)$$

Where $T_{x,y,m,t}$ and $P_{x,y,m,t}$ are the anomalies of temperature and precipitation respectively, at grid point $(x, y)$ in month $m$ and time $t$. $\alpha_{x,y,m}^T$ and $\alpha_{x,y,m}^P$ are the intercepts, representing baseline conditions during *El Niño* phases for a given month $m$. $\eta_{x,y,m}^T$ and $\eta_{x,y,m}^P$ represent the teleconnection strengths with $\bar{r}$ during *El Niño* phases for month $m$. $\gamma_{x,y,m}^T$ and $\gamma_{x,y,m}^P$ indicate the changes in baseline conditions during *La Niña* episodes compared to *El Niño*. $\delta^T x, y, m$ and $\delta^P x, y, m$ capture the changes in the teleconnection strengths during *La Niña* compared to *El Niño*. $\phi_{x,y,m}^T$ and $\phi_{x,y,m}^P$ are coefficients for the control variables in $c'_{x,y,m,t} \cdot v_{x,y,m,t}^T$ and $v_{x,y,m,t}^P$ are the error terms.

Estimation of the models in Equations 22 and 23 yields a distribution of 20 estimated coefficients (one per month from May of year $t-1$ through December of year $t$) for each ENSO phase (*El Niño* / *La Niña*) at each longitude-latitude grid point $(x, y)$, separately for temperature and precipitation. Our final teleconnection indices are obtained by averaging these coefficients across the 20 months. We only include coefficients that are statistically significant at the 95% level; coefficients that do not meet this threshold are set to zero
<figure><img src="image_26.png" /><figcaption>Figure A4: Global temperature teleconnection values and stability</figcaption></figure>

Note: Panels A and B display country-averaged temperature teleconnection patterns for El Niño and La Niña phases, respectively. Panel C shows the distribution of standard deviations for each grid cell's teleconnection index, computed over successive 42-year rolling windows. Panel D illustrates the temporal evolution of the spatial distribution of temperature teleconnections across these 42-year periods. Author's calculation based on the methodology of Liu et al. (2023) and Callahan and Mankin (2023).

before aggregation. For each weather variable (temperature T and precipitation P) and ENSO phase, the teleconnection indices can be written as:

$$ \tau_{x,y}^{\hat{\theta}} = \frac{1}{20} \sum_{m=1}^{20} \left( \eta_{x,y,m}^{T} + \delta_{x,y,m}^{T} \cdot d_{t}^{\hat{\theta}} \right) \qquad (24) $$

$$ \rho_{x,y}^{\hat{\theta}} = \frac{1}{20} \sum_{m=1}^{20} \left( \eta_{x,y,m}^{P} + \delta_{x,y,m}^{P} \cdot d_{t}^{\hat{\theta}} \right) \qquad (25) $$

These indices represent the averaged teleconnection effects of ENSO on temperature and precipitation anomalies at each grid point, capturing the asymmetric impacts during El Niño and La Niña phases.

We also estimate teleconnections to ENSO signals on a global scale, enabling direct comparison with the estimates provided by Liu et al. (2023) and Callahan and Mankin (2023), thereby validating our methodological framework. Figure A4 presents these global estimates. Panels A and B display country-averaged temperature teleconnection patterns that closely align with previously documented results, confirming that our approach accurately captures the established climatic responses to ENSO.

To assess temporal variability, we recalculated the ENSO-temperature teleconnections over successive 42-year sub-periods of the record. Panel C shows the distribution of standard deviations for each grid cell's teleconnection index computed over these rolling windows. A lower standard deviation indicates that the teleconnection value for a given location remains relatively stable over time, whereas higher values reflect greater temporal variability. In the histogram, most grid cells exhibit standard deviations below 0.015, with only a small fraction ranging from 0.02 to 0.035. This low level of variability quantitatively confirms that the ENSO-temperature teleconnection patterns are remarkably stable over time. The results in Panel D confirm that the teleconnection signals are largely stable over multi-decadal periods. The spatial patterns of ENSO's influence show only modest changes from one 42-year window to the next, suggesting that the global
teleconnection structure has persisted robustly over the observational record. This high temporal consistency is critical—it demonstrates that the characteristic ENSO warming/cooling footprints (as shown in Panels A and B) have been a reliable feature of the climate system over the past century, thereby reinforcing the overall reliability of our results.

# A2 Bank Data

## A2.1 General Outlay of Banking Data

The architecture of banks typically involves a hierarchical structure with different levels of operations and administrative functions. Here's a general overview:

**Headquarter and main office** The headquarters is the central hub of a bank's operations and administration. It houses top-level executives, management, and key decision-makers. Strategic planning, policy formulation, and major administrative functions are carried out at the headquarters. The main office is a large branch that serve as significant operational center within a specific city or town. It handle a broad range of banking services, including customer accounts, loans, and investments. The main office may have specialized units for services like wealth management, corporate banking, and commercial lending.

**Branches** Branches are the most common customer-facing entities of a bank, providing various financial services to the public. They are distributed across different locations, often within cities and towns, to make banking services accessible to a wider population. Branches handle day-to-day transactions, account management, and customer service. According to the Federal Deposit Insurance Corporation, a site must have a branch charter if deposits are received, or checks paid or money lent.

The bank data used throughout this paper rests on two primary sources: Call Reports and Summary of Deposits. Note that each bank has a unique identification number that works as a regulatory identification (RSSD ID) from the National Information Center (NIC). This identification number is used to trace the banks over the two datasets.

## A2.2 Call Reports

**Coverage** *bank × quarterly*.

**Source** Federal Financial Institutions Examination Council (FFIEC), Wharton Research Data Services (WRDS), and the Federal Reserve Bank of Chicago.

The *Consolidated Reports of Condition and Income* (Call Reports) in the United States are regulatory financial reports that banks are required to submit to federal banking authorities. These reports are a critical part of the supervisory and examination process conducted by regulatory agencies, primarily the Federal Deposit Insurance Corporation (FDIC), the Federal Reserve System, and the Office of the Comptroller of the Currency (OCC). Every bank is required to file a Call Report as of the close of business on the last day of each calendar quarter.

Call Report data provide detailed quarterly information on balance sheet items and income statements at the bank level from 1992 to 2023. The data can be extended further back to 1976 through the earlier *Report of Conditions and Income*, though variable definitions prior to the 1990s are often inconsistent. Moreover, our empirical design requires merging with the *Summary of Deposits* (SOD), which only became available in 1994. Importantly, Call Reports are consolidated at the bank level, offering a comprehensive view of the entire banking organization, including the main office and all associated branches.

Each data item in the Call Reports is identified by a unique MDRM (Micro Data Reference Manual) code, which combines a four-letter mnemonic indicating the data series with a numeric variable identifier. For
example, RCON2170 and RCFD2170 both refer to total assets, but the former captures domestic offices only, while the latter includes both domestic and foreign operations.

The specific Call Report form a bank files depends on its organizational structure and size. Currently, there are three versions. The **FFIEC 031** form is used by institutions with both domestic and foreign offices. It includes additional schedules—such as *Schedule RC-H* (Selected Balance Sheet Items for Domestic Offices) and *Schedule RC-I* (Assets and Liabilities of International Banking Facilities)—to capture the geographic breakdown of balance sheet positions. In contrast, the **FFIEC 041** form is used by banks operating solely within the United States and excludes these international reporting components. The **FFIEC 041** form was introduced in March 2001, replacing the earlier **FFIEC 032, 033**, and **034** forms, which had previously been used by domestic banks segmented by asset size. This consolidation improved data comparability and streamlined the reporting process. Finally, the **FFIEC 051** form was introduced in 2016 to reduce the regulatory burden for smaller institutions—those with domestic offices only and less than $5 billion in assets—by eliminating or reducing the frequency of less relevant items, while preserving core regulatory data.

<figure><img src="image_27.png" /><figcaption>**Figure A5:** Asset extension from large banks' filing FFIEC form 031</figcaption></figure>

Note: For large banks filing the FFIEC 031 form—used by institutions with both domestic and foreign offices—the domestic assets series (RCON2170) is no longer reported directly as of 2011. We reconstruct it using Schedule RC-H—Selected Balance Sheet Items for Domestic Offices, where RCON2170 equals the sum of RCON2192 (total assets excluding net due from foreign operations) and RCON2163 (net due from own foreign offices, Edge and Agreement subsidiaries, and IBFs). We verify the consistency of the reconstructed series with pre-2011 data to ensure comparability across reporting forms and over time.

These changes in form structure and variable definitions over time require careful harmonization to ensure consistency in financial time series. For example, large banks filing the **FFIEC 031** form—used by institutions with both domestic and foreign offices—ceased reporting the domestic assets series (RCON2170) directly as of 2011. However, this variable can be reconstructed using information from *Schedule RC-H—Selected Balance Sheet Items for Domestic Offices*, where RCON2170 is computed as the sum of RCON2192 (Total assets excluding net due from foreign offices, Edge and Agreement subsidiaries, and IBFs) and RCON2163 (Net due from own foreign offices, Edge and Agreement subsidiaries, and IBFs). In contrast, smaller banks using the **FFIEC 041** and **051** forms—which apply to banks with only domestic offices—continue to report RCON2170 directly. We validate the reconstructed series for consistency with historical data prior to 2011 to ensure that the definition and perimeter of domestic assets remain comparable across forms and over time (see Figure A5).
Additionally, some financial variables in the Call Reports are reported on a year-to-date basis, accumulating over the course of the year rather than reflecting standalone quarterly values. If left unadjusted, such reporting practices would introduce significant seasonal distortions. We therefore identified and transformed these variables to recover consistent quarterly observations. These harmonization efforts were essential for producing robust time-series data and form the basis for our empirical analysis. The complete structuring of variables and adjustments is documented in Table A2.

We restrict our analysis to commercial banks chartered in the contiguous United States (CONUS). While the broader financial sector includes a variety of institutions, commercial banks operate under a standardized regulatory framework with uniform capital and liquidity requirements, making them more directly comparable. They also play a central role in business lending and mortgage financing, making them particularly exposed to the economic disruptions induced by climate anomalies. Their lending portfolios—spanning corporate, real estate, and consumer loans—are directly affected by changes in borrower creditworthiness, collateral values, and regional economic conditions, all of which are shaped by climate variability. By focusing on commercial banks, we ensure a homogeneous sample that captures institutions operating under similar constraints and financial reporting structures. Other financial entities, such as credit unions or industrial banks, exhibit distinct business models and balance sheet structures that could introduce confounding effects unrelated to the mechanisms we investigate. Moreover, the geographical footprint of commercial banks makes them particularly relevant for assessing localized climate exposures, as they remain key providers of credit in climate-sensitive regions.

We include all commercial banks in our analysis, even if they fail during the period. Retaining only surviving banks would introduce survival bias, distorting the estimated effects of climate shocks. Additionally, many failed banks are absorbed through mergers, altering the structure of the sector. To account for these dynamics, we trace mergers over time using the *Uniform Bank Performance Report* (UBPR) definition, which identifies significant mergers as those involving a 25% or greater increase in assets. By incorporating failed and merged banks, we capture the full evolution of the banking sector and provide a more comprehensive assessment of climate-related risks.

## A2.3 Summary of Deposits

**Coverage** *branch × yearly*.

**Source** Federal Deposit Insurance Corporation (FDIC).

Accurately geolocating bank branches is essential for linking financial data to localized economic and environmental factors, yet substantial gaps exist in the geographic information provided by the FDIC dataset. While data on branch deposits are readily available, latitude and longitude coordinates are missing for a significant proportion of branches. In the 1990s, up to 50% of branches are unaccounted for in raw data, leaving large portions of deposits effectively unaccounted for in regional analyses. This issue persisted into the early 2010s, complicating efforts to precisely link banking activity with climate exposure.

To address these limitations, we implemented a three-layer geocoding methodology. First, we used the OpenStreetMap (OSM) API to retrieve full address coordinates. For unmatched cases, we refined results using the U.S. Census API. Finally, residual records were geocoded at the ZIP code level using OSM. This approach significantly enhances the spatial accuracy of the FDIC dataset, as illustrated in Figure A6, enabling more precise measurement of localized banking activity and its exposure to climate anomalies.

Beyond technical geolocation challenges, correctly assigning climate exposure to banks requires recognizing the dispersion of their operational activities. Unlike firms operating within a single geographic area, banks strategically locate branches to optimize market reach, customer base, and regulatory advantages. Headquarters locations often reflect tax or administrative considerations rather than operational concentration. For instance, Bank of America was headquartered in New York City until recently before relocating to Las Vegas, a move unrelated to its nationwide lending footprint. This distinction underscores the necessity of measuring climate exposure based on actual branch presence rather than relying solely on headquarters
<figure><img src="image_28.png" /><figcaption>Figure A6: Improved coverage of branch location with geocoding</figcaption></figure>

Note: This figure illustrates the enhanced geolocation coverage achieved through our three-layer geocoding methodology. Missing branch number or branch coordinates, affecting up to 50% of deposits in the 1990s, were systematically resolved using Open-StreetMap and U.S. Census APIs, with residual cases geocoded at the ZIP code level using OSM API. This improvement ensures a more accurate linkage between banking activity and climate exposure.

locations.

Consequently, regional climate shocks affect banks only to the extent that they have business operations in the impacted area. While macro-level studies often assume homogeneous sector-wide exposure, individual bank-level analyses must account for the geographic dispersion of banking activities. A commonly employed methodology in banking research is to weight climate shocks by a bank's local business exposure, typically using mortgage origination or local deposit shares (e.g. Cortés and Strahan, 2017; Koetter et al., 2020; Rehbein and Ongena, 2022). However, mortgage origination may not be entirely exogenous to climate exposure, as lending decisions adjust dynamically to evolving risk conditions.

By leveraging an improved geocoding framework and deposit-weighted exposure measures, our approach provides a more precise mapping of bank-level climate risk. The geolocation enhancements documented in Figure A6 ensure that we accurately capture the full scope of regional banking activity, strengthening the reliability of our analysis of climate-induced financial disruptions.

Figure 3 displays the resulting spatial distribution of bank branches and headquarters across the United States in 2023, based on FDIC Summary of Deposits data. Covering nearly the entire commercial banking sector, the figure reflects the population footprint, with a clear concentration of activity along the eastern part of the contiguous United States.
A3 Variable overview

<table><caption>Table A1: Summary Statistics</caption><thead><tr><th></th><th>Mean</th><th>25<sup>th</sup></th><th>50<sup>th</sup></th><th>75<sup>th</sup></th><th>σ</th><th>Min</th><th>Max</th><th>Obs.</th></tr></thead><tbody><tr><td><strong>Activity concentration</strong></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Number of bank branches</td><td>10.96</td><td>1</td><td>3</td><td>6</td><td>109.8</td><td>1</td><td>6581</td><td>802,054</td></tr><tr><td>Number of States of bank op.</td><td>1.126</td><td>1</td><td>1</td><td>1</td><td>0.978</td><td>1</td><td>49</td><td>802,054</td></tr><tr><td>Number of counties of bank op.</td><td>2.769</td><td>1</td><td>1</td><td>2</td><td>6.901</td><td>1</td><td>209</td><td>802,054</td></tr><tr><td><strong>Climate exposure</strong></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>ENSO Phase (θ)</td><td>172.6</td><td>97.13</td><td>173.2</td><td>249.8</td><td>82.10</td><td>18.12</td><td>322.7</td><td>778,528</td></tr><tr><td>ENSO Magnitude (r)</td><td>-0.026</td><td>-1.156</td><td>-0.289</td><td>1.074</td><td>1.647</td><td>-3.043</td><td>5.179</td><td>778,528</td></tr><tr><td>Teleconnection El Niño (temperature)</td><td>0.026</td><td>0.001</td><td>0.024</td><td>0.058</td><td>0.046</td><td>-0.157</td><td>0.151</td><td>772,730</td></tr><tr><td>Teleconnection La Niña (temperature)</td><td>0.087</td><td>0.072</td><td>0.102</td><td>0.126</td><td>0.061</td><td>-0.210</td><td>0.189</td><td>772,730</td></tr><tr><td>Teleconnection El Niño (precipitation)</td><td>0.005</td><td>-0.026</td><td>0.004</td><td>0.032</td><td>0.041</td><td>-0.105</td><td>0.128</td><td>772,730</td></tr><tr><td>Teleconnection La Niña (precipitation)</td><td>-0.014</td><td>-0.039</td><td>-0.015</td><td>0.008</td><td>0.036</td><td>-0.112</td><td>0.111</td><td>772,730</td></tr><tr><td><strong>Financial variables</strong></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Bank's distance to default (Z-score)</td><td>176.3</td><td>72.30</td><td>135.9</td><td>232.6</td><td>154.0</td><td>-0.039</td><td>1326</td><td>783,458</td></tr><tr><td>Non performing loan ratio (NPL)</td><td>0.008</td><td>0.001</td><td>0.004</td><td>0.010</td><td>0.013</td><td>0.000</td><td>0.148</td><td>801,806</td></tr><tr><td>Foreclosure ratio (FOR)</td><td>0.003</td><td>0.000</td><td>0.000</td><td>0.002</td><td>0.008</td><td>0.000</td><td>0.111</td><td>801,809</td></tr><tr><td>Return on assets (ROA)</td><td>0.002</td><td>0.002</td><td>0.003</td><td>0.004</td><td>0.004</td><td>-0.041</td><td>0.055</td><td>800,028</td></tr><tr><td>Cost-to-income ratio (CIR)</td><td>0.700</td><td>0.568</td><td>0.657</td><td>0.763</td><td>0.319</td><td>0.000</td><td>5.881</td><td>800,187</td></tr><tr><td>Total equity ratio (EQ)</td><td>0.111</td><td>0.084</td><td>0.099</td><td>0.120</td><td>0.065</td><td>0.017</td><td>0.927</td><td>801,810</td></tr><tr><td>Deposit ratio</td><td>0.836</td><td>0.814</td><td>0.859</td><td>0.890</td><td>0.099</td><td>0.002</td><td>0.967</td><td>801,810</td></tr><tr><td>Gross loan ratio</td><td>0.612</td><td>0.519</td><td>0.634</td><td>0.729</td><td>0.163</td><td>0.000</td><td>0.964</td><td>801,810</td></tr><tr><td>Real estate loan ratio</td><td>0.394</td><td>0.270</td><td>0.395</td><td>0.519</td><td>0.174</td><td>0.000</td><td>0.866</td><td>801,810</td></tr><tr><td>Total assets, log</td><td>11.77</td><td>10.85</td><td>11.62</td><td>12.48</td><td>1.401</td><td>0.000</td><td>21.67</td><td>801,810</td></tr></tbody></table>

Note: This table presents summary statistics for the main variables used in the analysis. Bank-level financial data are drawn from the Consolidated Reports of Condition and Income (Call Reports) covering the period 1994–2023. ENSO phase and magnitude are measured using the Unified ENSO Climate Index (UECI). Teleconnection indicators for temperature and precipitation are constructed following the methodologies of Callahan and Mankin (2023) and Liu et al. (2023). All variables are winsorized at the 0.1% and 99.9% levels to reduce the influence of outliers. For descriptive purposes, ENSO magnitude ($r_t$) is presented as a continuous variable, with negative values corresponding to La Niña events.
<table><caption>Table A2: Main Variables, Descriptions, and Sources</caption><thead><tr><td>Variable Name</td><td>Description and Source</td></tr></thead><tbody><tr><td>theta</td><td>Phase of the quarterly UECI-based ENSO signal, ranging from 0° to 360°. Identifies El Niño (θ > 180°) and La Niña (θ < 180°) phases. Derived from NOAA, authors' calculation based on UECI method of Zhang et al. (2019).</td></tr><tr><td>r</td><td>Magnitude of the quarterly ENSO signal, centered at 0 by adjusting its sign based on θ. Serves as the benchmark variable for ENSO magnitude. Derived from NOAA, authors' calculation based on UECI method of Zhang et al. (2019).</td></tr><tr><td>tmp_beta_nino</td><td>Teleconnection coefficient for temperature anomalies during El Niño events. Authors' calculations based on the methods of Callahan and Mankin (2023) and Liu et al. (2023).</td></tr><tr><td>tmp beta_nina</td><td>Teleconnection coefficient for temperature anomalies during La Niña events. Authors' calculations based on the methods of Callahan and Mankin (2023) and Liu et al. (2023).</td></tr><tr><td>prc beta_nino</td><td>Teleconnection coefficient for precipitation anomalies during El Niño events. Authors' calculations based on the methods of Callahan and Mankin (2023) and Liu et al. (2023).</td></tr><tr><td>prc beta_nina</td><td>Teleconnection coefficient for precipitation anomalies during La Niña events. Authors' calculations based on the methods of Callahan and Mankin (2023) and Liu et al. (2023).</td></tr><tr><td>branch nbr</td><td>Number of branches operated by a bank. Derived from Summary of Deposits (FDIC).</td></tr><tr><td>states nbr</td><td>Number of U.S. states where a bank operates branches. Based on Summary of Deposits (FDIC).</td></tr><tr><td>county nbr</td><td>Number of counties where a bank operates branches, reflecting regional diversification. Derived from Summary of Deposits (FDIC) data.</td></tr><tr><td>npl</td><td>Non-performing loan ratio: captures the quality of a bank's loan portfolio, defined as the sum of loans 90+ days past due and non-accrual loans, divided by total assets. Loans 90+ days past due includes loans overdue by more than 90 days but still accruing interest. For domestic-only banks (Form 041), this is derived from RCON1407 for periods prior to 2011. From 2011 onwards, RCON1407 is no longer available and is reconstructed as the sum of the following subcomponents: RCONF174, RCONF175, RCON3494, RCON5399, RCONC237, RCONC239, RCON3500, RCONF180, RCONF181, RCONB835, RCON1607, RCONB576, RCONK214, RCONK217, RCON5390, RCON5460, and RCON1227. For consolidated banks (Form 031), the equivalent adjustments are made using the same series. Non-accrual loans represent loans that have stopped accruing interest due to repayment concerns. For Form 041, this is RCON1403 prior to 2011. After 2011, RCON1403 is no longer directly available and is reconstructed as the sum of similar subcomponents. Similar adjustments apply for consolidated banks (Form 031). Source: FFIEC, WRDS, Chicago Fed.</td></tr><tr><td>fr</td><td>Foreclosure ratio: real estate owned (RCON2150) divided by total assets (RCON2170). Represents foreclosed properties as a share of assets. Source: FFIEC, WRDS, Chicago Fed.</td></tr><tr><td>roa</td><td>Return on assets: net income (RIAD4340, transformed from year-to-date to quarterly) divided by total assets (RCON2170). Source: FFIEC, WRDS, Chicago Fed.</td></tr><tr><td>cir</td><td>Cost-to-income ratio: operating expenses (RIAD4093) divided by the sum of net-interest revenue (RIAD4107 - RIAD4074) and other operating income (RIAD4079). Source: FFIEC, WRDS, Chicago Fed.</td></tr><tr><td>eq</td><td>Total equity-to-assets ratio: total equity (RCON3210) divided by total assets (RCON2170). Source: FFIEC, WRDS, Chicago Fed.</td></tr><tr><td>zscore</td><td>Z-score: sum of equity-to-assets ratio and ROA, normalized by ROA's rolling 8-quarter standard deviation. Equity and assets data come from RCON3210 and RCON2170, respectively. Historical data ensure full computation starting in 1994. Source: FFIEC, WRDS, Chicago Fed.</td></tr><tr><td>rela</td><td>Ratio of real estate loans to total assets. Derived from RCON1410 for periods prior to 2011. After 2011, RCON1410 is obtained as the sum of RCONF158, RCONF159, RCON1420, RCON1797, RCON5367, RCON5368, RCON1460, RCONF160, and RCONF161. Source: FFIEC, WRDS, Chicago Fed.</td></tr><tr><td>loana</td><td>Gross loan ratio: gross loans (RCON1400) divided by total assets (RCON2170). Source: FFIEC, WRDS, Chicago Fed.</td></tr><tr><td>size</td><td>Bank size, measured as the natural logarithm of total assets (RCON2170). For large banks reporting with Form 031, total assets are reconstructed as the sum of RCON2192 (domestic assets) and RCON2163 (net due from foreign offices). Source: FFIEC, WRDS, Chicago Fed.</td></tr><tr><td>merger</td><td>Significant mergers, defined as events resulting in a 25% or greater increase in total assets (RCON2170) over one year. Source: FFIEC, WRDS, Chicago Fed.</td></tr></tbody></table>
A4 Ruling-Out Alternative Explanations

This appendix presents a comprehensive set of robustness exercises aimed at addressing potential alternative
explanations for the baseline results. Further details on the various estimation strategies are provided in
Section 8. We begin by testing the sensitivity of our findings to the measurement of climate shocks, replacing
the continuous UECI variable with a binary shock definition (Figure A7). Next, we incorporate fixed effects
at the Federal Reserve District level (Figure A8) and at the state level (Figure A9) to account for potential
regulatory heterogeneity.

To address concerns about survival bias, we re-estimate the model on the subsample of continuously oper-
ating banks (Figure A10) and include a survival dummy (Figure A11). We also examine whether broader
differences in bank health drive the results by including CAMELS-based financial indicators as controls
(Figure A12). To rule out confounding from the 2008 subprime mortgage crisis, we estimate specifications
excluding the subprime period control (Figure A13) and restricting the sample to the pre-crisis period (Fig-
ure A14). We further assess the role of seasonal fluctuations in banking data by re-estimating the models
without the moving average transformation of the dependent variables (Figure A15). Finally, to ensure
that our estimates are not confounded by broader macroeconomic cycles, we control for county-level GDP
growth, inflation, and unemployment (Figure A16).

Across all exercises, the estimated responses remain qualitatively consistent, reinforcing the conclusion that ENSO-induced effects on banking stability are robust to alternative model specifications and potential con-founders. Further details on each robustness check are provided in Section 8 of the main text.

<figure><img src="image_29.png" /><figcaption>Figure A7: Baseline estimates' robustness, binary *El Niño* and *La Niña* shocks</figcaption></figure>

Note: IRFs from equation (4) estimated via Local Projections (Jordà and Taylor, 2025). Shaded areas show 90% confidence intervals with bank-level clustered robust standard errors. Results in pp., except Z-score (in %).
<figure><img src="image_30.png" /><figcaption>**Figure A8:** Baseline estimates' robustness with fixed effects at the Federal Reserve District level.</figcaption></figure>

Note: IRFs from equation (4) estimated via Local Projections (Jordà and Taylor, 2025). Shaded areas show 90% confidence intervals with bank-level clustered robust standard errors. Results in pp., except Z-score (in %).

<figure><img src="image_31.png" /><figcaption>**Figure A9:** Baseline estimates' robustness with fixed effects at the state level.</figcaption></figure>

Note: IRFs from equation (4) estimated via Local Projections (Jordà and Taylor, 2025). Shaded areas show 90% confidence intervals with bank-level clustered robust standard errors. Results in pp., except Z-score (in %).

<figure><img src="image_32.png" /><figcaption>**Figure A10:** Baseline estimates' robustness focusing on a sub-sample of survival banks.</figcaption></figure>

Note: IRFs from equation (4) estimated via Local Projections (Jordà and Taylor, 2025). Shaded areas show 90% confidence intervals with bank-level clustered robust standard errors. Results in pp., except Z-score (in %).
<figure><img src="image_33.png" /><figcaption>Figure A11: Baseline estimates' robustness with a survival banks dummy.</figcaption></figure>

Note: IRFs from equation (4) estimated via Local Projections (Jordà and Taylor, 2025). Shaded areas show 90% confidence intervals with bank-level clustered robust standard errors. Results in pp., except Z-score (in %).

<figure><img src="image_34.png" /><figcaption>Figure A12: Baseline estimates' robustness controlling for CAMELS indicators.</figcaption></figure>

Note: IRFs from equation (4) estimated via Local Projections (Jordà and Taylor, 2025). Shaded areas show 90% confidence intervals with bank-level clustered robust standard errors. Results in pp., except Z-score (in %).

<figure><img src="image_35.png" /><figcaption>Figure A13: Baseline estimates' robustness dropping the NBER-defined subprime crisis control.</figcaption></figure>

Note: IRFs from equation (4) estimated via Local Projections (Jordà and Taylor, 2025). Shaded areas show 90% confidence intervals with bank-level clustered robust standard errors. Results in pp., except Z-score (in %).