## 2 Data Overview  
### Gridded Numerical Data (IMPROVER)  
The gridded numerical data used in this study are from the state-of-the-art probabilistic ensemble post-processing and verification system known as IMPROVER (Integrated Model post-PROcessing and VERification). IMPROVER ingests and blends raw forecasts from various NWP models, applies gridded calibration, and generates fully probabilistic forecasts [30]. The Australian version of IMPROVER developed by the Bureau of Meteorology has a grid covering the Australia region and adopts a Cartesian coordinate system. The gridded data have a spatial resolution of 4.8 km at surface level (2 m above ground level) and 9.6 km on upper levels. The model generates hourly forecasts every 6 h (4 issues a day) with lead hours up to 192. Figure 1a shows a snapshot of IMPROVER gridded forecast temperature at screen level on 24 August 2022 with lead hour 2.0, which shows the extent of the spatial coverage of IMPROVER data. Since this current study focuses on deterministic forecasts only, the expected value of the IMPROVER probabilistic forecast at each location is extracted for training ML models (early investigations showed that taking various straightforward ensemble approaches did not improve the skill of the forecast or properly capture the distribution. Further research is left for future work). In this study, the grid value of selected variables at 11 Australian sites (Figure 1b) up to lead day 7 are extracted from each issue forecast from IMPROVER, forming hourly time series as shown in Figure 1c. The variables selected as features are temperature at screen level (\(T\)), dew point temperature at screen level (\(T_{\text{d}}\)), and wind vector \(u\) and \(v\) at 10 m height. The wind vectors are selected as features because wind, particularly at coastal locations, is known to affect the temperature in the local region through both advection (transporting air from a cooler or warmer region) and through vertical processeswhich are not fully resolved in the upstream model. As such, conditioning the temperature and dew point predictions on the wind \(u\) and \(v\) components is likely to improve the model's performance. Upper layer variables are not included since the spatial resolution of those variables is lower. Available IMPROVER data at time of this study range from August 2022 to May 2023 (incorporating Spring, Summer and Autumn). The sites selected for this study are scattered in different parts of Australia to ensure that ML model accuracy is not location dependent. Rainfall climatology varies significantly across these sites, including hot, dry locations, colder and wetter locations, and varying coastal proximity. Pointwise IMPROVER root-mean-square error (RMSE) of same-day forecasts on the selected sites in the studied period range from 1.2-2.3 \({}^{\circ}\)C for temperature, and 1.2-2.9 \({}^{\circ}\)C for dew point temperature. One important property of IMPROVER forecasts that affects ML training strategy is that the IMPROVER forecast error generally grows by lead hour. This is expected as NWP models derive much of their predictability from the initial conditions, and so gradually lose accuracy with longer forecast lead time. To quantify and visualize this effect, Figure 2a shows the IMPROVER temperature forecast of a single site \(T_{\text{N}}\) in October with respect to the actual observed temperature for each forecast (\(T_{\text{O}}\)). The scatter plot shows that while numerical forecasts on both lead day 0 (2-24 h) and lead day 8 (168-192 h) exhibit a positive correlation with observed values, the forecast at lead day 0 is more accurate and less scattered. Figure 2b shows the distribution of forecast error for the same data points,  
Figure 1: (**a**) IMPROVER gridded data, (**b**) selected sites and (**c**) extracted daily time series from gridded data for Alice Springs.