[PERSON] et al. [18] reported that, based on data from China FLUXNET sites (China Flux Research Observation Network), the LUE-GPP model, which considers the influence of diffuse radiation on _LUE_, performed better than the MODIS GPP model in different ecosystems. Considering the transmission of scattered light in the canopy and the difference between the _LUE_\({}_{\rm max}\) (_LUE_ without any environmental constraints) of shaded and sunlit leaves, [PERSON] et al. [9] proposed a two-leaf LUE-GPP model to separately calculate the _GPP_ of shaded and sunlit leaves and reported more reliable simulation results with those of the EC measurements. [PERSON] et al. [19] assessed the global performance of ten LUE-GPP models and considered the effect of diffuse radiation in FLUXNET sites. They revealed that the LUE models incorporating diffuse radiation effects accounted for 46.7-63.6% of the daily variability in GPP, as measured by the EC sites. Moreover, the performance of the LUE-GPP models varied across different ecosystems [19], highlighting the importance of evaluating and analyzing model performance to enhance the application of LUE-GPP models in agroforestry ecosystems. The introduction of temperature (\(T_{\rm s}\)) and moisture constraints (\(W_{\rm s}\)) and their combination are the main sources causing the different model performances [20]. For example, the MODIS model used vapor pressure deficit (_VPD_) to quantify the limitation of atmospheric drought on _LUE_\({}_{\rm max}\)[21], whereas the VPM used the land surface water index (_LSWI_) to characterize the underlying moisture limitation on _LUE_\({}_{\rm max}\)[13]. To quantify the effect of temperature on _LUE_\({}_{\rm max}\), MODIS used a linear function, whereas the EF-LUE model used a quadratic function [22]. In terms of model structure, the EF-LUE model assumes that _LUE_\({}_{\rm max}\) is mainly affected by the maximum constraints of moisture or temperature, while the MODIS model assumes that _LUE_\({}_{\rm max}\) is jointly affected by both moisture and temperature. Analysis of the model structure and the main environmental constraints is the premise for the practical application of the model and the basis for improving future models [20]. Photosynthetic active radiation (_PAR_) is an important variable in the LUE-GPP model. The presence of clouds and aerosols in the atmosphere reduces _PAR_ reaching the canopy [23]. As the cloud thickness and aerosol concentration increase, direct _PAR_ decreases, whereas diffuse _PAR_ at the canopy increases [24; 25]. Changes in ecosystem radiation components greatly affect the ecosystem carbon flux and, consequently, _LUE_[24; 26]. Numerous studies have reported that _LUE_ increases with an increase in cloud thickness [27; 28]. Affected by the Southeast and Southwest monsoons, moist air streams originating from the South ChinaSea and Bay of Bengal converge here owing to the obstruction of the mountains, resulting in frequent cloudy and rainy weather conditions in Chengdu Plain, which is the most important agricultural region in Southwest China [29]. However, the response of agroecosystem LUE to different weather conditions and its effect on the performance of LUE-GPP models have not been explored. Thus, quantifying the performance of models under different weather conditions could provide valuable information on agroecosystem \(GPP\) model evaluation in humid regions. This study aimed to (1) evaluate the applicability of various big-leaf and two-leaf LUE-GPP models, (2) determine the effect of a single environmental constraint (\(T_{\rm s}\) or \(W_{\rm s}\)) and their combination on estimating ecosystem GPP, and (3) assess model performance under different weather conditions and improve model accuracy by incorporating weather variables into the big-leaf and two-leaf LUE-GPP algorithms.