Harmonized Land Dynamics in Regional Climate Simulations and Projection: A LUH2-Based RegCM-CLM Study over Iran

Land use and land cover change can significantly affect regional climate variables through land-atmosphere interactions. When these changes are not represented dynamically, models may underestimate key feedbacks, especially in regions undergoing rapid transformation. Despite the importance of land surface dynamics, most regional climate studies continue to use static land datasets and ignore evolving processes. To address this limitation, we employed the RegCM4.7 regional climate model coupled with the CLM4.5 land surface model over Iran. The model was run using both the default static land data configuration and the annually updated Land Use Harmonization version 2 (LUH2) datasets in place of the default configuration. Temperature and precipitation outputs were assessed for a historical period and two future intervals, using two shared socioeconomic pathway scenarios. Findings reveal that the application of Land Use Harmonization datasets leads to more accurate temperature outputs, particularly for daily minimum values. The dynamic configuration shows stronger warming trends and better agreement with ERA5 data. Based on the comparison with static land representation, temperature projections using dynamic LUH2 data are warmer by approximately 0.13°C to 1.2°C under SSP2-4.5 and 0.02°C to 0.26°C under SSP5-8.5 on a seasonal scale. These results suggest that the Sixth Assessment Report temperature projections may be optimistic, as incorporating dynamic land-change data leads to higher warming estimates. Although the static configuration performs better in simulating precipitation, the dynamic model provides a more detailed framework for long-term projections. Due to the dominant influence of large-scale atmospheric systems, land dynamics did not significantly improve precipitation outcomes. However, the adaptability of dynamic LULC data offers greater potential for capturing future variability and extremes under changing climate conditions. Overall, the findings underscore the importance of including land surface forcing in regional climate modeling, particularly for temperature and precipitation.