We propose GeoIS, a new importance sampling method for estimating rare event probabilities for an underlying Gaussian distribution that exploits asymptotic theory and local event geometry. The proposal distribution is a mixture of two components. The first component draws a Gaussian along the boundary near the instanton and adds to it an exponentially distributed shift into the rare event. This component is derived from the known asymptotic approximation to the optimal zero-variance proposal distribution. The second mixture component is designed to cover missing event mass due to boundary-approximation mismatch, thereby preserving unbiasedness. We rigorously analyze this sampling strategy's performance as a function of event geometry, dimension, rarity level, and number $N$ of importance samples. Under explicit and relatively mild conditions on the former three features, we prove that the squared relative error of GeoIS is bounded by $c/N$ with high probability, for an absolute constant $c$. We implement our method numerically in several examples, including a diffusion PDE problem. The numerical experiments exhibit the qualitative dependence on geometry, dimension, rarity and sample size predicted by the theory, and show that GeoIS markedly outperforms importance sampling methods that use a fully Gaussian proposal distribution.

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