Description: A factorial inferential grids grouping and representativeness analysis ( FIGGRA ) approach is developed to achieve a systematic selection of representative grids in large-scale climate change impact assessment and adaptation ( LSCCIAA ) studies and other fields of earth and space sciences. FIGGRA is applied to representative-grids selection for temperature ( Tas ) and precipitation ( Pr ) over the Loess Plateau ( LP ) to verify methodological effectiveness. FIGGRA is effective at and outperforms existing grid selection approaches (e.g. self-organizing maps) in multiple aspects such as clustering similar grids, differentiating dissimilar grids, and identifying representative grids for both Tas and Pr over LP . In comparison with Pr , the lower spatial heterogeneity and higher spatial discontinuity of Tas over LP leads to higher within-group similarity, lower between-group dissimilarity, lower grids grouping effectiveness, and higher grid representativeness; the lower inter-annual variability of the spatial distributions of Tas results in lower impacts of the inter-annual variability on the effectiveness of FIGGRA . For LP , the spatial climatic heterogeneity is the highest in January for Pr and in October for Tas ; it decreases from Spring, Autumn, Summer to Winter for Tas and from Summer, Spring, Autumn to Winter for Pr . Two parameters, i.e. the statistical significance level ( α ) and the minimum number of grids in every climate zone ( Nmin ), and their joint effects are significant for the effectiveness of FIGGRA ; normalization of a non-normal climate-variable distribution is helpful for the effectiveness only for Pr . For FIGGRA -based LSCCIAA studies, a low value of Nmin is recommended for both Pr and Tas , and a high and medium value of α for Pr and Tas , respectively.