Singular disruptive events like solar eclipses affect the measured values of meteorological variables at the earth’s surface. To quantify such an impact, it is necessary to estimate what value the parameter would have taken had the event not occurred. We design and compare several methods to perform such an estimate based on longer observational timeseries from individual meteorological surface stations. Our methods are based on regularised regressions (including a Bayesian variant) and provide both a point an associated error estimate of the disruptive event’s impact. With their help, we study the effect of the total solar eclipse of July 2nd, 2019, in the Coquimbo Region of Chile, on near-surface air temperatures and winds. The observational data used have been collected by the meteorological surface station network of the Centro de Estudios Avanzados en Zonas Áridas (CEAZA). Most stations inside the eclipse’s umbra registered a temperature drop of 1–2 ∘C, while the most extreme estimated temperature drop surpassed 6 ∘C. The presence of an ‘eclipse cyclone’ can neither be proven nor refuted. Application of the regression methods to other comparable problems like volcanic eruptions, forest fires, or simply gap filling of observational data, are conceivable.