Incorporating seasonal climate insights in time series forecasting problems, such as demand predictions, can help inform planning and optimizing operations. Current time series forecasting approaches incorporate deterministic short-term weather attributes as exogenous inputs. However, encoding the relationship between seasonal climate and demand is challenging due to the uncertain nature of seasonal predictions and their associated spatio-temporal variability and predictive skills. Recently, time series research has introduced a deep learning-based temporal fusion transformer (TFT) model using self-attention for modelling different types of time series. In this work, we incorporate seasonal climate predictions in TFT and experimentally observe that forecast errors can be reduced by 5-17% on real-world dataset while forecasting up to few months ahead.