Uncertainty‐Aware Machine Learning for Onset of Deep Convection: Under what Conditions Are Trigger Predictions More Reliable?

Abstract Machine Learning (ML) models have emerged as a powerful tool for predicting deep convection triggering, yet the atmospheric conditions that systematically challenge these models in detecting deep convection remain poorly understood. To diagnose such ambiguous regimes, we trained a Controlled Abstention Neural Network (CAN) that separates high‐ and low‐confidence predictions, enabling the physical characterization of uncertain environments over the Southern Great Plains during the warm season. Results show that low‐confidence, abstained predictions cluster in environments with weak‐to‐moderate mid‐level vertical velocity and low dynamic generation rate of Convective Available Potential Energy (CAPE), predominantly corresponding to short‐lived convective episodes in locally forced, non‐equilibrium regimes with weaker synoptic forcing. In contrast, confident predictions correspond to long‐lived, equilibrium regimes driven by synoptic forcing of ascent and enhanced low‐level southerly flow. Our findings provide physically grounded guidance for identifying forecast situations that require additional caution in operational ML‐based convection triggering.