The article examines the limitations of current Machine Learning (ML) models in weather prediction, focusing on their physical consistency and fidelity. Key findings include: 1. **Spectral Diagnostics**: ML models produce forecasts with significantly different energy spectra compared to reanalysis fields and physics-based models. This results in overly smooth predictions, particularly for spatial scales shorter than 300-400 km, and a lack of realistic representation of fundamental physical balances and derived quantities. 2. **Physical Consistency**: ML models fail to accurately represent sub-synoptic and mesoscale weather phenomena, leading to unrealistic forecasts of geostrophic wind balance, rotational and divergent wind components, and vertical motions. 3. **Forecast Skill and Physical Realism**: While ML models show improved forecast skill over traditional physics-based models, this is often at the cost of physical realism. The reduced spectral energy and heteroscedasticity in forecast errors suggest that ML models may not be suitable for ensemble prediction systems. 4. **Future Directions**: Balancing forecast skill and physical realism will be crucial for future ML models. The article suggests that ML models can be better understood as post-processing algorithms rather than general-purpose atmosphere simulators, and that they may be more effective for medium to extended-range forecasts where the main drivers of predictability are on larger scales. The study highlights the need for more research to develop ML models that can produce both skillful and physically consistent forecasts at all relevant spatial scales.The article examines the limitations of current Machine Learning (ML) models in weather prediction, focusing on their physical consistency and fidelity. Key findings include: 1. **Spectral Diagnostics**: ML models produce forecasts with significantly different energy spectra compared to reanalysis fields and physics-based models. This results in overly smooth predictions, particularly for spatial scales shorter than 300-400 km, and a lack of realistic representation of fundamental physical balances and derived quantities. 2. **Physical Consistency**: ML models fail to accurately represent sub-synoptic and mesoscale weather phenomena, leading to unrealistic forecasts of geostrophic wind balance, rotational and divergent wind components, and vertical motions. 3. **Forecast Skill and Physical Realism**: While ML models show improved forecast skill over traditional physics-based models, this is often at the cost of physical realism. The reduced spectral energy and heteroscedasticity in forecast errors suggest that ML models may not be suitable for ensemble prediction systems. 4. **Future Directions**: Balancing forecast skill and physical realism will be crucial for future ML models. The article suggests that ML models can be better understood as post-processing algorithms rather than general-purpose atmosphere simulators, and that they may be more effective for medium to extended-range forecasts where the main drivers of predictability are on larger scales. The study highlights the need for more research to develop ML models that can produce both skillful and physically consistent forecasts at all relevant spatial scales.