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<p>Clouds strongly modulate the top-of-the-atmosphere energy budget and are a major source of uncertainty in climate projections. “Cloud controlling factor” (CCF) analysis derives relationships between large-scale meteorological drivers and cloud radiative anomalies, which can be used to constrain cloud feedback. However, the choice of meteorological CCFs is crucial for a meaningful constraint. While there is rich literature investigating ideal CCF setups for low-level clouds, there is a lack of analogous research explicitly targeting high clouds. Here, we use ridge regression to systematically evaluate the addition of five candidate CCFs to previously established core CCFs within large spatial domains to predict longwave high-cloud radiative anomalies: upper-tropospheric static stability (<span class="inline-formula"><i>S</i>_UT</span>), sub-cloud moist static energy, convective available potential energy, convective inhibition, and upper-tropospheric wind shear (<span class="inline-formula">Δ<i>U</i>₃₀₀</span>). We identify an optimal configuration for predicting high-cloud radiative anomalies that includes <span class="inline-formula"><i>S</i>_UT</span> and <span class="inline-formula">Δ<i>U</i>₃₀₀</span> and show that spatial domain size is more important than the selection of CCFs for predictive skill. We also find an important discrepancy between the optimal domain sizes required for predicting locally and globally aggregated radiative anomalies. Finally, we scientifically interpret the ridge regression coefficients, where we show that <span class="inline-formula"><i>S</i>_UT</span> captures physical drivers of known high-cloud feedbacks and deduce that the inclusion of <span class="inline-formula"><i>S</i>_UT</span> into observational constraint frameworks may reduce uncertainty associated with changes in anvil cloud amount as a function of climate change. Therefore, we highlight <span class="inline-formula"><i>S</i>_UT</span> as an important CCF for high clouds and longwave cloud feedback.</p>