An experimental study on flow boiling pressure drop and heat transfer of a new environmentally friendly refrigerant R1233zd(E), in a parallel multi-microchannel evaporator was carried out. The silicon microchannels evaporator was 10 mm long and 10 mm wide, having 67 parallel channels, each 100 x 100 mu m(2), separated by a fin with a thickness of 50 mu m. Upstream of each channel, a micro orifice was placed to stabilize the two-phase flow and to obtain good flow distribution. The operating conditions for flow boiling tests were: mass fluxes from 500 to 2750 kg m(-2) S-1, heat fluxes from 6 to 50 W cm(-2), inlet subcooling of 5.8 K, and a nominal outlet saturation temperature of 35 degrees C for stable flow boiling. The test section's backside base temperatures were measured by an infrared (IR) camera. The stable flow boiling data without back flow was selected through flow visualization recorded by a highspeed camera coupled with a microscope. These data were then used to assess the applicability of existing two-phase pressure drop models, and to further develop a new empirical model suitable for the high mass flux operating conditions. This new pressure drop model was used to predict the local fluid temperature for the further heat transfer data identification. The fine-resolution local heat transfer coefficients were obtained by solving the three-dimensional inverse heat conduction problem. The experimental results showed that in the saturated flow boiling region the local heat transfer coefficient first decreased moderately in the very low vapor quality region (x < 0.05), then increased significantly but monotonically along the flow direction. The fine-resolution local heat transfer data at the saturated flow boiling region were compared with two groups of heat transfer correlations. The first one considered the flow boiling mechanism occurring in muliti-microchannels as a combination of nucleate boiling and forced convection boiling, while the other one associated this mechanism to liquid thin film evaporation, thus indicating a controversy. It is found that the flow pattern based model belonging to the second group yielded the best agreement with the experimental data, predicting 92.0% of this new database within 30%. (C) 2016 Elsevier Ltd. All rights reserved.