Precipitation nowcasting is crucial for weather-dependent decision- making in various sectors, providing accurate and high-resolution predictions of precipitation within a typical two-hour timeframe. Deep learning techniques have shown promise in improving now- casting accuracy by leveraging large radar datasets. However, accurately predicting heavy rainfall events remains challenging due to several persistent problems in previous work. These include spatial-temporal misalignment between meteorological information and precipitation data, as well as the performance gap be- tween different rainfall levels. To address these challenges, we propose two innovative modules: Spatial-Temporal Aligned Meteorological INformation Attention (STAMINA) and Focal Precip Loss (FPL). STAMINA integrates meteorological information using spatial-temporal embedding and pixelwise linear attention mechanisms to overcome spatial-temporal misalignment. FPL addresses event imbalance through event weighting and a penalty mechanism. Through extensive experiments, we demonstrate significant performance improvements achieved by STAMINA and FPL, with an 8% improvement in predicting light rainfall and, more significantly, a 30% improvement in heavy rainfall compared to the state-of-the-art DGMR model. These modules offer practical and effective solutions for enhancing nowcasting accuracy, with a specific focus on improv- ing predictions for heavy rainfall events. By tackling the persistent problems in previous work, our proposed approach represents a significant advancement in the field of precipitation nowcasting.