<figure><img src="image_36.png" /><figcaption>Figure A14: Baseline estimates' robustness restricting the sample to pre-crisis period.</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_37.png" /><figcaption>Figure A15: Baseline estimates' robustness to seasonality by removing the moving average transformation on the dependent variable.</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_38.png" /><figcaption>Figure A16: Baseline estimates' robustness to macroeconomic business cycles including real GDP growth, inflation, and unemployment rates, all evaluated at the county 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 %).
A5 Falsification Test: Precipitation Teleconnections

In this section, we use precipitation-based teleconnections as a falsification test to assess the robustness of our main findings. While the primary analysis relies on temperature anomalies—captured by $\tau^0$—as the dominant transmission channel from ENSO to banking stability, we extend our local exposure estimation to precipitation anomalies, denoted $\rho^0$, which represent a theoretically plausible but empirically weaker channel. Precipitation anomalies could, in principle, affect bank performance through disruptions in agriculture, infrastructure, or regional economic activity. However, compared to temperature patterns, ENSO-induced precipitation anomalies in the U.S. are significantly more volatile, spatially fragmented, and economically diffuse. If our identification strategy is valid and the estimated effects are indeed driven by meaningful climatic transmission mechanisms, then using $\rho^0$ in place of $\tau^0$ should yield substantially weaker—or null— effects on bank outcomes. As such, this exercise provides a useful placebo, helping to rule out spurious correlations and lending further support to a causal interpretation of the temperature-based results.

<figure><img src="image_39.png" /><figcaption>**Figure A17:** Nonlinear ENSO teleconnections and precipitation anomalies across the United States</figcaption></figure>

Note: This map illustrates the pixelwise teleconnection indices, denoted as $\rho_{x,y}^{\text{nina}}$ for El Niño events (A) and $\rho_{x,y}^{\text{nina}}$ for La Niña events (B). These indices represent the strength and direction of ENSO's influence on precipitation 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 precipitation 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).

Figure A17 presents the spatial distribution of precipitation teleconnection coefficients across the contiguous United States. As anticipated, these coefficients are substantially noisier than their temperature-based counterparts, reflecting the inherently more localized, volatile, and stochastic nature of precipitation anomalies. Unlike temperature deviations, which tend to persist over broad spatial scales, precipitation patterns are shaped by microclimatic variation and are less likely to exert systematic, large-scale effects on financial outcomes. This reinforces the role of this exercise as a falsification test—designed to verify whether the observed relationship between ENSO shocks and banking instability holds when an alternative, but less plausible, climate exposure channel is used. The absence of strong effects when relying on precipitation-based exposures would lend additional credibility to the temperature-driven mechanism identified in the main analysis.

**Precipitation exposure intensity.** The first exercise examines the intensity of exposure, measured through the absolute value of the precipitation teleconnection index, $|\rho_{i,t}^0|$. By interacting the magnitude of the ENSO phase-specific shock, $r_t^0$, with the absolute bank-specific precipitation teleconnection, this exercise captures the intensity of disruptions experienced by each bank. The use of the absolute value abstracts from the anomaly's specific nature—whether it represents an increase or decrease in precipitation—and instead eval-
<table><caption>**Table A3:** Average precipitation teleconnection by bank size</caption><thead><tr><th></th><th>Small banks<br>(&lt;1bn)</th><th>Large banks<br>(10-25bn)</th><th>All banks<br>(≥ 25bn)</th></tr></thead><tbody><tr><td>ρ<sup>nino</sup><sub>i,t</sub></td><td>0.004 (0.041)</td><td>0.007 (0.039)</td><td>0.012 (0.034)</td><td>0.014 (0.029)</td><td>0.005 (0.041)</td></tr><tr><td>ρ<sup>nina</sup><sub>i,t</sub></td><td>-0.014 (0.036)</td><td>-0.013 (0.032)</td><td>-0.011 (0.029)</td><td>-0.013 (0.023)</td><td>-0.014 (0.036)</td></tr></tbody></table>

Note: This table reports average precipitation teleconnection estimates $\rho_{i,t}^{\theta}$ 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.

uates the overall strength of ENSO-induced disruptions. The corresponding specification is:

$$ \Delta_h y_{i,t+h}^k = \lambda_h^k y_{i,t-1}^k + \omega_h^k (r_t \times |\rho_{i,t}^{nino}|) + \alpha_h^k (r_t \times |\rho_{i,t}^{nina}|) \\ + \Gamma_h^k X_{i,t} + \phi_{h,i}^k + q_{h,t}^k + u_{h,i,t}^k \quad (26) $$

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

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

$$ \rho_{i,t}^{\theta+} = \begin{cases} \rho_{i,t}^{\theta} & \text{if } \rho_{i,t}^{\theta} > 0 \\ 0 & \text{otherwise} \end{cases} \qquad \rho_{i,t}^{\theta-} = \begin{cases} \rho_{i,t}^{\theta} & \text{if } \rho_{i,t}^{\theta} < 0 \\ 0 & \text{otherwise} \end{cases} \quad (27) $$

This disaggregation allows for a detailed analysis of how precipitation anomalies—whether excess rainfall or drought conditions—affect banking outcomes. The corresponding specification is:

$$ \Delta_h y_{i,t+h}^k = \lambda_h^k y_{i,t-1}^k + \omega_{1,h}^k (r_t \times \rho_{i,t}^{nino-}) + \alpha_{1,h}^k (r_t \times \rho_{i,t}^{nina-}) \\ + \omega_{2,h}^k (r_t \times \rho_{i,t}^{nino+}) + \alpha_{2,h}^k (r_t \times \rho_{i,t}^{nina+}) \\ + \Gamma_h^k X_{i,t} + \phi_{h,i}^k + q_{h,t}^k + u_{h,i,t}^k \quad (28) $$

**Results.** Figures A18 and A19 report the results for the precipitation exposure intensity exercise, while Figures A20 and A21 examine the effects of positive and negative precipitation anomalies separately. Across all specifications, the estimated effects of precipitation-based teleconnections are considerably smaller in magnitude and far noisier than those obtained using temperature-based exposure. This outcome is consistent with expectations: precipitation patterns are inherently more localized and volatile, making it less likely that they exert systematic effects on banking outcomes at the aggregate level.

Importantly, these null or weak effects reinforce the interpretation of the precipitation-based analysis as a placebo test. If our main findings were driven by spurious correlations or omitted variable bias, we would expect similarly strong patterns to emerge when applying the same empirical strategy to precipitation exposures. The fact that such patterns do not materialize lends further credibility to our identification strategy and supports the conclusion that temperature anomalies—rather than precipitation fluctuations—constitute the dominant transmission channel through which ENSO shocks affect banking stability.
<figure><img src="image_40.png" /><figcaption>Figure A18: *El Niño* shocks by teleconnection intensity to precipitation 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_41.png" /><figcaption>Figure A19: *La Niña* shocks by teleconnection intensity to precipitation 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_42.png" /><figcaption>Figure A20: *El Niño* shocks by teleconnection direction to precipitation 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_43.png" /><figcaption>Figure A21: *La Niña* shocks by teleconnection direction to precipitation 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 (%).
A6 The Liquidity Channel

Climate shocks can affect banking stability through several financial transmission mechanisms. The primary channels involve credit risk and bank performance, as climate-induced disruptions impair borrowers’ ability to repay loans, often due to lower incomes or declining asset values. These pressures manifest in higher rates of non-performing loans (NPLs) and foreclosures, which erode bank profitability and capital buffers, ultimately threatening solvency (Noth and Schüwer, 2023). In contrast, liquidity risk—while particularly relevant in developing economies with underdeveloped financial systems and limited deposit insurance (Brei et al., 2019; Chabot and Bertrand, 2023)—is less likely to play a central role in advanced economies like the U.S., where deposit insurance schemes and regulatory safeguards help prevent destabilizing withdrawals. Consistent with this distinction, our main analysis concentrates on credit risk and performance as the dominant pathways linking climate shocks to financial fragility. Nonetheless, to compelment the main finding from this study, we report the full set of estimates assessing the potential role of the liquidity channel.

Figure A22 Panel A presents results from the baseline specification (equation (4)), using the deposit-to-
assets ratio as a proxy for liquidity risk—this ratio reflects the share of stable, short-term funding on which
banks rely. Following *El Niño* episodes, we observe a modest and transitory rise in deposit ratios, with
changes that are economically negligible. In the case of *La Niña*, where broader signs of financial distress
are evident, the estimated increase reaches 0.6pp—approximately a 0.7% rise in the deposit ratio—which
likely reflects ex post insurance payouts or public assistance. These results indicate that ENSO shocks do
not trigger liquidity stress in U.S. banks as we don’t observe panic-driven withdrawals.

Panel B builds on the local climate exposure estimates introduced in Section 6, focusing on the intensity of exposure. Specifically, we interact the absolute value of the teleconnection index, $|\tau_{i,t}^{\theta}|$, with the phase-specific ENSO shock magnitude $r_{i,t}^{\theta}$, following equation (7). This interaction captures the extent of disruption experienced by each bank, regardless of the direction of the anomaly. The results reaffirm the baseline findings: *La Niña* episodes are associated with a more pronounced, yet still limited, increase in deposit ratios. Moreover, banks with stronger local climate exposure exhibit a slightly larger deposit response, consistent with precautionary inflows or external support rather than liquidity stress. These patterns support our broader conclusion that liquidity risk is not the primary transmission channel.

Panel C turns to the type of temperature anomaly, decomposing the teleconnection index into positive ($\tau_{i,t}^{\theta+}$) and negative ($\tau_{i,t}^{\theta-}$) components as outlined in equation (9). The results show broadly symmetric responses for both warm and cold deviations, with no meaningful evidence of deposit outflows or destabilizing liquidity effects. Taken together, these robustness checks confirm that ENSO-induced climate shocks do not significantly affect bank liquidity, reinforcing our focus on credit risk and profitability as the key transmission channels.

Panels D and E replicate the local climate exposure exercises using precipitation-based teleconnection in-
dices, serving as a complementary placebo test (see Section A5 for more details). These estimates assess
whether ENSO-induced disruptions to banking stability could plausibly operate through channels other than
temperature anomalies. Following equations (26) and (28), we construct exposure measures based on the
intensity and direction of precipitation teleconnections. Consistent with the inherently volatile and spatially
fragmented nature of precipitation patterns, the estimated coefficients are markedly noisier and less spa-
tially coherent than their temperature-based counterparts. The regression results confirm the absence of
systematic or economically significant effects on liquidity risk. These findings further reinforce the con-
clusion that temperature anomalies—rather than precipitation shocks—constitute the primary transmission
channel linking ENSO events to adverse financial outcomes in the U.S. banking system.
A - Baseline Specification

<figure><img src="image_44.png" /></figure>

B - Temperature Intensity

<figure><img src="image_45.png" /></figure>

<figure><img src="image_46.png" /></figure>

C - Temperature Anomalies

<figure><img src="image_47.png" /></figure>

<figure><img src="image_48.png" /></figure>

D - Precipitation Intensity

<figure><img src="image_49.png" /></figure>

<figure><img src="image_50.png" /></figure>

E - Precipitation Anomalies

<figure><img src="image_51.png" /></figure>

<figure><img src="image_52.png" /><figcaption>Figure A22: ENSO shocks and the liquidity channel of bank instability</figcaption></figure>

Note: Panel A displays IRFs from equation (4); Panel B from equation (7); Panel C from equation (9); Panel D from equation (26); and Panel E from equation (28). All IRFs are estimated using the Local Projections method (Jordà and Taylor, 2025). Shaded areas represent 90% confidence intervals, with robust standard errors clustered at the bank level to account for intra-group dependence and ensure valid inference. IRFs show cumulative changes expressed in percentage points (pp), except for the Z-score, which is reported as cumulative percentage changes (%).