To enhance the precision of short-term regional weather forecasts with the Rapid-refresh Multi-scale Analysis and Prediction System-Urban subsystem (RMAPS-Urban), an urbanized land data assimilation system RMAPS-LDAS with a spatial resolution of 1 km is developed by coupling the Noah land surface model with a multi-layer urban model, namely, the building effect parameterization and building energy model (BEP–BEM), enabling improved representation of land surface elements for both natural and urban underlying surfaces. RMAPS-LDAS runs offline utilizing merged meteorological data from high-density in-situ observations and a gridded meteorological dataset generated via thin-plate smoothing spline models. The structure of the coupled Noah and BEP–BEM model is modified to enhance computational and storage efficiency, making RMAPS-LDAS operationally applicable for regional weather forecasts. The urban layer (roof, wall, and road) temperatures require a spin-up period of no more than seven days, significantly shorter than that needed for soil states. Evaluation over the Beijing–Tianjin–Hebei region show that RMAPS-LDAS reduces the root-mean-square error (RMSE) and bias of simulated soil temperature by 47.8% and 92.3%, respectively, compared with ECMWF soil temperature forecasts. The system reduces RMSE and absolute bias in soil moisture simulations by 6.5% and 18.2%, respectively. Comparisons with observed road and building temperatures confirm that the BEP–BEM model realistically captures the thermal evolution of urban surfaces. Coupling RMAPS-LDAS with RMAPS-Urban led to a 9.6% reduction in RMSE for 2-m specific humidity forecasts, although a slight increase in air temperature RMSE (0.54%) was observed. However, for urban areas, the coupling reduced air temperature RMSE by 4.9%. Overall, the impact on wind speed prediction remains relatively minor.

Urban population growth and the expansion of built-up areas are placing increasing pressure on energy systems in large cities. Anthropogenic heat from dense buildings further intensifies local climate differences and impacts building energy use. However, existing urban building energy models (UBEMs) lack the ability to differentiate micro-scale environments or capture energy variations across local climate zones (LCZs). To address this, we developed a UBEM tool driven by localized weather data (LWD), which flexibly defines weather grid resolution and simulates building energy use in specific urban contexts. Focusing on a 3.34 km² campus in Beijing with 880 buildings and 170 LCZs at 200 m resolution, the study analyzes the impact of summer and winter urban heat island (UHI) effects on local weather and energy demand. Machine learning models explore how urban morphology influences local weather and how building form affects energy use. Results show that UHI significantly increases cooling degree days (CDD) in dense urban areas and exacerbates climate disparities between different local environments. The CDD in the hotspot is four times higher than in suburban areas, while heating degree days (HDD) are reduced by more than half. Street aspect ratio and floor area ratio are key at the cluster scale; building shape coefficient (BSC) and height dominate at the building scale. Heating demand is especially sensitive to BSC. Ignoring UHI, the bias in total energy use intensity (EUI) results for poorly insulated buildings can be 3 to 10 times higher than that for other typical buildings. Except for large offices and hospitals, in Beijing, UHI tends to reduce total annual energy use for most building types. Using the integrated urban canopy model (UCM) and UBEM simulation tool and the XGBoost-SHAP computational design method enables more accurate prediction and response to climate–building interactions, offering a quantitative basis for integrated urban design strategies. This supports improved regulation of building spaces and envelopes in response to seasonal UHI variations.

Based on the building height and density data on a 100-m resolution, hourly 2-m temperature and humidity data at 83 automatic weather stations, and gridded local climate zone (LCZ) data on a 120-m resolution in urban Beijing in 2020, this study first employs the semivariogram combined with building parameters to calculate spatial correlations and has identified an LCZ grid resolution of 500 m suitable for best usage of the available observation data. Then, how the spatially heterogeneous LCZs affect and contribute to the canopy urban heat island intensity (UHII) and urban dry island intensity (UDII) are quantitatively investigated. It is found that UHII is high in winter and low in summer with a unimodal diurnal variation while UDI is low in winter but high in summer with a bimodal diurnal variation. The LCZ with compact mid-rise (open high-rise) buildings exhibits the highest UHII (UDII), followed by the compact high-rise (compact low-rise), while the LCZ of scattered trees presents both the lowest UHII and the lowest UDII. The most significant difference in the UHII (UDII) among the nine LCZ types in the urban area of Beijing is 2.62°C (1.1 g kg⁻¹). Area-weighted averaging analysis reveals that the open mid-rise LCZ is the most significant contributor to the UHII (UDII), immediately followed by compact mid-rise (open low-rise), with the least contribution from bare rock or paved (scattered trees). The results also indicate that beyond the intrinsic physical properties of the LCZs of a city, their area proportions cannot be overlooked in evaluating their impact on the UHI and UDI. These quantitatively findings could help urban planners to create a livable urban climate and environment by adjusting the relevant land use.