Localized temperature (T) and relative humidity (RH) in bulk peanut warehouses can differ from warehouse-average conditions, limiting single-point monitoring. This study retrospectively evaluated a temporal-leakage-controlled sensor-network workflow for forecasting warehouse microclimate and reproducing rule-defined advisory states. Environmental records from 18 locations in a commercial-scale in-shell peanut warehouse were harmonized to 15 min intervals from 22 October 2025 to 16 March 2026. Persistence, a leakage-controlled 24 h seasonal-naive baseline, a development-selected persistence-seasonal blend, and an attention-based long short-term memory (LSTM) benchmark were compared using blocked train-core, internal-development, and final-test partitions separated by 48 h embargoes. Final-test metrics used strict raw-observed targets and aggregated errors across all 192 forecast leads over the full 48 h horizon. Seasonal naive had the lowest RH mean absolute error (MAE; 4.15 percentage points), the blend had the lowest T MAE (1.92 °C), and persistence had the lowest dew-point MAE (1.58 °C); attention-LSTM had higher MAE than all temporal baselines. Across 13 non-overlapping 48 h backtests, the blend achieved 80.2% advisory-state agreement, 83.4% Danger recall, and an 8.65% false-safe rate. Final-test raw support covered 11 of 18 locations. The workflow is a forecasting and advisory-screening benchmark, not validated automated control or direct prediction of kernel moisture, fungal activity, aflatoxin, or peanut quality. The findings provide a basis for testing forecast-assisted inspection prioritization, with particular attention to the less predictable upper air void.