Zero-shot forecasting predicts variables at locations or conditions without direct historical data, a challenge for traditional methods due to limited location-specific information. We introduce a retrieval-augmented model that leverages spatial correlations and temporal frequencies to enhance predictive accuracy in unmonitored areas. By decomposing signals into different frequencies, the model incorporates external knowledge for improved forecasts. Unlike large foundational time series models, our approach explicitly captures spatial-temporal relationships, enabling more accurate, localized predictions. Applied to microclimate forecasting, our model outperforms traditional and foundational models, offering a more robust solution for zero-shot scenarios.