Explaining monthly precipitation anomalies in northwestern South America by integrating vertical dynamics and energetics

In dynamically limited environments such as the coastal Northwestern South America (NWSA), thermodynamic instability (ΔT) alone is insufficient to autonomously trigger deep convection due to persistent background subsidence. The Buoyancy Work Rate (BWR) addresses this boundary constraint by vertically coupling ΔT with vertical velocity (ω). A causal discovery analysis empirically verified this formulation, demonstrating that the BWR preserves the dominant causal route to precipitation over other individual thermodynamic or dynamic variables, successfully capturing the dynamic trigger required to realize convection. As a diagnostic proxy, the BWR systematically outperforms established thermodynamic indices because it explicitly quantifies the realized mesoscale atmospheric response rather than just the precursor potential. This diagnostic advantage is reinforced by an upper tail dependence analysis based on empirical copulas, which confirmed the index's robustness during extreme hydroclimatic anomalies. Moreover, while isolated mid-level vertical velocity dictates instantaneous convection, its high-frequency volatility limits its utility for continuous monitoring. By integrating ΔT, the BWR mathematically inherits the thermal inertia of the oceanic boundary conditions, imparting greater signal persistence and making it a more stable proxy for sub-seasonal operative monitoring. Ultimately, the operational value of the BWR emerges in its prognostic application. By substituting predicted precipitation anomalies from the SEAS5 model with the predicted BWR anomalies, the predictions overcome the limitations of the sub-grid parameterizations inherent in the climate model. Relying instead on explicitly resolved mesoscale variables governed by primitive equations, the BWR systematically improves seasonal predictive skill during the peak of the austral summer and mitigates the forecast degradation caused by the boreal spring predictability barrier.