Data of variability and joint variability of global crop yields and their association with climate

We present the output data of Robust Principal Component Analysis (RPCA) applied to global crop yield variability of maize, rice, sorghum and soybean (MRSS) as presented in the publication "Climate drives variability and joint variability of global crop yields" (Najafi et al., 2019). Global maps of the correlation between all the principal components (PCs) acquired from the low rank matrix (L) of MRSS and Palmer Drought Severity Index (PDSI), air temperature anomalies (ATa) and sea surface temperature anomalies (SSTa) are provided in this article. We present co-varying countries, impacted cropland areas across global countries, and 10 global regions by climate and the association between PCs and multiple atmospheric and oceanic indices. Moreover, the joint dependency between PCs of MRSS yields are presented using two different approaches.