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This paper combines CMIP5 historical simulations and observations of surface temperature to investigate relative contributions of forced and internal climate variability to long-term climate trends. A suite of estimated forced signals based on surrogate multi-model ensembles mimicking the statistical characteristics of individual models is used to show that, in contrast to earlier claims, scaled versions of the multi-model ensemble mean cannot adequately characterize the full spectrum of CMIP5 forced responses, due to misrepresenting the model uncertainty. The same suite of multiple forced signals is also used to derive unbiased estimates of the model simulated internal variability in historical simulations and, after appropriate scaling to match the observed climate sensitivity, to estimate the internal component of climate variability in the observed temperatures. On average, climate models simulate the non-uniform warming of Northern Hemisphere mean surface temperature well, but are overly sensitive to forcing in the North Atlantic and North Pacific, where the simulations have to be scaled back to match observed trends. In contrast, the simulated internal variability is much weaker than observed. There is no evidence of coupling between the model simulated forced signals and internal variability, suggesting that their underlying dominant physical mechanisms are different. Analysis of regional contributions to the recent global warming hiatus points to the presence of a hemispheric mode of internal climate variability, rather than to internal processes local to the Pacific Ocean. Large discrepancies between present estimates of the simulated and observed multidecadal internal climate variability suggest that our ability to attribute and predict climate change using current generation of climate models is limited.