TEMPO Exposes Systematic Deficiencies in Simulating Daylight Formaldehyde Variability During the 2024 Growing Season: A Classification‐Based Evaluation of WRF‐CMAQ

Abstract Formaldehyde (HCHO) serves as a useful tracer of tropospheric photochemistry, linking volatile organic compound emissions to atmospheric oxidation capacity and ozone production. The Tropospheric Emissions: Monitoring of Pollution (TEMPO) geostationary satellite provides unprecedented hourly daylight observations of HCHO across North America. Taking a geographically agnostic approach, we classify 1.2 million TEMPO hourly profiles from the May to September 2024 growing season into six daylight evolution patterns: (i) northeastern forests with steady accumulation, (ii) southeastern isoprene/anthropogenic hotspots with dome‐shaped patterns, (iii) southwestern arid regions with morning peaks and afternoon decline, (iv) coastal environments with decreasing patterns, (v) background with minimal variability (50% of observations), and (vi) oil/gas/agricultural regions with pronounced noon peaks. This data‐driven classification provides a physically coherent framework for model evaluation to partially avoid error cancellation in conventional regional averaging. Weather Research and Forecasting‐Community Multiscale Air Quality agrees well with TEMPO spatial patterns and monthly mean magnitudes within 10% for values above 1 × 10 16 molec/cm 2 during peak growing season months, but substantially underestimates HCHO columns (30%–50%) in the southwestern arid region and Permian Basin where anthropogenic emissions are poorly constrained. TEMPO suggests that the largest daylight HCHO amplitudes occur over high‐NO X /high‐isoprene environments. The model uniformly underestimates the daylight range by 3–5 × 10 15 molec/cm 2 across all surface types, with variability too flat across most regimes except the southeastern isoprene hotspot. This deficit suggests missing or underrepresented HCHO precursors, insufficient OH daylight range, and inadequate HCHO yields in CB06r7. These findings demonstrate that TEMPO’s hourly observations expose systematic model deficiencies that remained hidden under the temporal constraints of conventional observing systems.