It is worth noting that the estimated cloud effects, despite their broad agreement in the global pattern across datasets, can differ in magnitude and even in sign in certain regions (e.g., the inconsistent cloud effects and their seasonality in Central Africa), suggesting cloud **data** are a key uncertainty source for our analysis. The cloud effects of forests estimated in a recent study 58 based on a different MODIS-derived cloud dataset 59 (produced by a different retrieval algorithm) revealed a similar global dominance of cloud enhancement and regional prevalence of cloud inhibition (e.g., in Amazon). Yet regional inconsistencies remained for Southeast US and Central Africa, where the identified cloud inhibition could be weakened or absent with alternative cloud **data** (ref. 58 and Supplementary Fig. 13). This emphasizes the need of using multi-source cloud **data** to improve the robustness of the estimated cloud effects while reducing the uncertainty from data. Nevertheless, the occurrence of cloud inhibition in these regions is indirectly supported by lower sensible heat over forests, which is in line with the mesoscale circulation mechanism, as well as the consistency between potential cloud inhibition and the actual cloud increase over forest loss locations (i.e., Southeast US hotspot). While more direct observational evidence is always desirable to help resolve inconsistencies, the lack of observations in regions like Central Africa, which have received less attention than the Amazon, hinders comparisons against other available evidence. This highlights the importance of dedicated observational efforts in specific regions, especially those understudied, to provide complementary information to our global-scale analysis.